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Xilinx girl geeks: Jayashree Rangarajan, Lori Pouquette, Jennifer Wong and Ambs Kesavan discuss the latest trends in accelerating computation for real-time machine learning at Xilinx Girl Geek Dinner in San Jose, California.

Speakers:
Eva Condron-Wells / Senior Manager of Talent Development / Xilinx
Niyati Shah / Senior Software Engineer / Xilinx
Changyi Su / Staff Design Engineer / Xilinx
Uma Madhugiri Dayananda / Senior Software Engineer / Xilinx
Tom Wurtz / Senior Director, Documentation & Program Management / Xilinx
Ambs Kesavan / Senior Director, Software Infrastructure Engineering & DevOps / Xilinx
Lori Pouquette / VP, Global Customer Operations / Xilinx
Jayashree Rangarajan / Senior Director, Software Development / Xilinx
Jennifer Wong / VP, FPGA Product Development / Xilinx
Angie Chang / CEO & Founder / Girl Geek X

Transcript of Xilinx Girl Geek Dinner – Lightning Talks & Panel:

Angie Chang: Okay. Here I go. Hi, thanks to you all for coming to Xilinx. My name is Angie Chang and I’m the founder of Girl Geek X. How many of you here, it’s your first Girl Geek Dinner? Okay. A good amount. All right, so Girl Geeks Dinners have been happening up and down the San Francisco Bay Area for the last 10 years. About every week, we’re in a different company. And I’m really excited to be here tonight at Xilinx.

Angie Chang: Personally, I was really excited when I looked up the website and I was like “I can’t wait to come here, check out the technology”. And the demos have been really awesome, and I encourage you to hangout and look at them. And walking down that hall of patents is very inspiring. And of course the food was amazing.

Angie Chang: So, thank you to everyone for coming. One thing I want to encourage everyone who comes to Girl Geek Dinner is to network. And I know that’s a kind of scary term, we’re always told to network. But one thing that I always try to do, even as an introvert, is to meet people. At least one or two people over a dinner, when you guys are chewing your food, just talk to people, ask them what they’re doing, if there’s anything you could do for them, and if there’s anything they could do for you. Because people are often looking for new opportunities, they’re looking to learn about the technology and also just to make friends.

Angie Chang: ‘Cause when you have friends in your workplace, or in your industry, that’s how we can stay in tech. We all know that women are dropping out of the workforce over time, and that’s really why we continue to do these dinners, is to encourage woman to continue coming out, meeting each other, encourage each other, and helping each other stay leaning in or whatever you like to call it, to your career.

Angie Chang: So please enjoy yourselves, and thank you again, Xilinx, for hosting.

Senior Manager of Talent Development Eva Condron-Wells welcomes the crowd to Xilinx Girl Geek Dinner in San Jose, California.

Eva Condron-Wells: Wonderful, thank you so much, Angie.

Eva Condron-Wells: So welcome to Xilinx. My name is Eva Condron-Wells and am a senior manager in human resources. I have the pleasure of helping employees at Xilinx listen, give, and feel as though they belong.

Eva Condron-Wells: I’m a social scientist by background, and I grew up in this very specific niche of the semiconductor industry, at another well known company. Most of my career has been in this particular slice of the semiconductor industry. And I think, in this moment, I just want to reflect on the fact that we have many things to be grateful for. And I want to start this evening with thanking a number of people.

Eva Condron-Wells: So first and foremost, thank you to the Girl Geek team for creating this type of forum for us to connect. So thank you so much, Girl Geek. Thank you to our Xilinx greeters, gurus, and guides. I hope that they’re making your visit meaningful. If you haven’t already seen other aspects of our campus, we welcome you to join us in the demo room later on. And, of course our guests, right? So you took time out of your day to come meet us, we know that you work hard and we’re all different types of Girl Geeks, but regardless of that difference, we’re here together to celebrate that brilliance of who you are and share who we are. So thank you for taking time out of your busy days to join us.

Eva Condron-Wells: And speaking of Xilinx, how many of you knew what Xilinx is or does before you saw the Girl Geek invitation? Raise your hands.

Eva Condron-Wells: Wow, that’s pretty impressive. You must be in fairly technical households. Because Xilinx, sometimes called X-links, but we help people understand how to pronounce it, isn’t necessarily a household name. That said, we affect the lives of people everyday. As we’re nested in technology that’s used broadly around the world, affecting everyone’s lives.

Eva Condron-Wells: Xilinx is a 35 year old company. Founded, what I like to say, on friendship. It was founded on friendship and a very incredible idea that is continuing to create many innovations, now and in the future. Our mission is to build the adaptable intelligent world. We have 4,000 employees plus, worldwide, and 4,000 plus patents. Giving us a one-to-one ratio of people to patents.

Eva Condron-Wells: When I articulate the pride I have in where I work, and who I work with, I genuinely mean we have some of the most brilliant people in the world. And you will get to hear from some of them tonight.

Eva Condron-Wells: So we invented the field programmable gate array. Not something everyone hears everyday, but this is a device that makes the life of an engineer easier. It speeds up their ability to perform their responsibilities and have chips work in various ways, right, thousands of applications can be used. But these engineers are empowered to reprogram chips in the field in hours instead of waiting weeks to get a completely new product. So this is the power of what we were founded on, the concept of enablement, empowerment, and acceleration.

Eva Condron-Wells: We have over 60 industry firsts, and our latest product, Alvio… Which is absolutely gorgeous, and is in our demo room, you can see the board… Is plugged into data centers, giving a 90x acceleration over a CPU. This nested technology is inside of thousands of products, changing the way we live, love, work, and play.

Eva Condron-Wells: We are not only diverse in our thought, that creates this technology, we are seekers of innovation, and we welcome brilliant minds who want to play in this space as well.

Eva Condron-Wells: Tonight our focus is on acceleration. You will gain insights into acceleration by the people who enable it. We will have three lightning talks, a panel, and Q&A where we look forward to hearing your questions to dive a little bit deeper into this topic. After 8 pm, we’ll transition to desert for those of you who haven’t already taken advantage of that. Some more demos, just around the corner, and some more networking.

Eva Condron-Wells: This is a very special evening for us, and we’re thrilled to have you. Now onto acceleration. Let’s let the lightning talks being. May I have our first presenter? So please welcome one of our first of three presenters, Niyati Shah. Thank you so much, Niyati.

Senior Software Engineer Niyati Shah gives a talk on compilers for adaptable compute acceleration at Xilinx Girl Geek Dinner.

Niyati Shah: Good evening, everyone. So let me begin my introducing myself. My name is Niyati and I work in the logic optimization group. I primarily focus on software architecture and [inaudible] design. Outside of work, I enjoy weightlifting, and traveling, and doing what I love, both at work and outside is what makes me a girl geek.

Niyati Shah: So let me start by asking you all a question. How many of you here are hardware engineers? Brilliant. And how many are software engineers? Perfect.

Niyati Shah: So, as we can see right in this room, we have a range of distribution of engineers from hardware background and software background. And that, I believe, presents the direction in which Xilinx has been headed. Traditionally Xilinx is a semiconductor and FPG chip company, and most of our customers used to be hardware developers. But as we are moved into the data center and acceleration markets, more of our customers are coming from software backgrounds.

Niyati Shah: And, what I’d like to do today, is give you an overview of the tools that we have that help our developers, regardless of their background, use our FPS to run their designs.

Niyati Shah: So we have the Hardware Developers who code using RTL, Verilog, VHDL. And for them, we have our signature product, which is the Vivado Design Suite. Next we have our Hardware Aware System Software Developers, who use C/C++ and SystemC. And for them, we provide them the Vivado HLS Compiler. We also have our Software Application Developers, who tend to use FPS mostly for accelerating their products or their designs. And for them, we have the SDAccel environment. And finally, for our Data Scientists who use frameworks such Caffe and TensorFlow, we have the AI Compilers or Edge Compilers.

Niyati Shah: In the next few slides, I’m going to go into a little bit more detail onto each of these different tools, so that you can get an idea of how they meet the needs of the targeted developers.

Niyati Shah: So as I mentioned, most of our hardware engineers work with RTL, Verilog, VHDL. But their figure is a piece of hardware, and it’s only going to understand binary numbers or bitstream. And so we have to take the RTL through a process to generate that bitstream. And the best analogy that I could give is kind of a translator. So, if you and your neighbor were to speak in, say, different languages, and the translator will go from your language and convert it to a language that your neighbor can understand. And so we take the RTL through a similar process. We start with synthesis. But [inaudible] the RTL, we apply synthesis. And the job of synthesis is to create a logical netlist , which is technology mapped to the targeted FPGA.

Niyati Shah: Once that is complete, then we need to optimize the netlist. And that’s where I come in. My team and I work on optimizing the logical netlist. We’ve optimized the design for power, area, timing, depending on the developer need. And finally create an optimized netlist, which will better use the resources that are on the FPGA.

Niyati Shah: Once that part is complete, we run placement. And the placement essentially takes these logical blocks here, and puts them on physical locations on the FPGA. Finally, we have the router which connects those physical blocks together. And after placement and routing are complete, we have a bitstream that we load on the FPGA to run the design.

Niyati Shah: Now, instead of starting an RTL, if our developers were to start with say, C/C++ or SystemC, then an additional step gets added because we have to first convert those languages to RTL before they can be fed into the backend tools.

Niyati Shah: And that’s where our Vivado HLS Compiler steps in. The HLS Compiler provides an eclipse ID, and allows our customers to design and develop using our product… The Vivado HLS Compiler and be part of this setting on top of the Vivado Design Suite. So, once the C/C++ is converted into RTL then we feed it into the Vivado Design Suite and generate a bitstream. And that allows us to provide a comprehensive solution for people starting with C/C++ or SystemC.

Niyati Shah: Now so far the tools I have talked about address the needs of our hardware developers. But for our software application developers, we also have a tool, which is our SDAccel Environment. The SDAccel Environment and Compiler sits on top of the existing Vivado HLS Compiler and the Vivado Design Suite, and allow users with no FPGA background, no hardware expertise, to take their designs and run them on our FPGAs. They will also allow us to support heterogeneous applications. So most of our software application developers are trying to accelerate their designs using the FPGAs. And so the designs will have a software component and they will have a hardware component. And the best example I can give you, is that of computer vision.

Niyati Shah: So I’m sure some of you have security cameras at home. And, in that case, you know that there are multiple parts to that. There is recording live video, but there is also object detection, where it will tell you there’s your dog is running around outside, or say there’s a robber in your house. And in that case, the object detection part is what gets accelerated using hardware.

Niyati Shah: The SDAccel Environment provides multiple different tools to our customers. We have a debugger, profiler, libraries ranging all the the way from low level mat libraries to high performing DSP libraries, but the star of the show is the compiler. The compiler helps us provide a comprehensive solution for both the software parts and for the hardware parts. The compiler compiles the software part so that it can be ran on the x86 machine. And it compiles all the individual hardware components, the kernels, so that they can go onto the FPGA. The kernels are compiled using the xocc compiler, which internally leverages the Vivado HLS Compiler and the Vivado Design Suite to generate bitstream for the parts that need to be accelerated.

Niyati Shah: So once the compilation is complete, then the software part will run on the [inaudible] machine, whereas the hardware part, which are the kernels, will run on the FPGA and they will communicate with used each of their using Xilinx runtime tools.

Niyati Shah: Now this SDAccel Environment and compilers are very crucial and critical building blocks in our next set of compilers, which are aimed towards our data scientists, who provide us models and frameworks such as Caffe and TensorFlow.

Niyati Shah: Our AI Compilers… The object here is to take the models that the customers have provided and optimize them with Deep Neural Network pruning and quantization, so that we can get optimized models. And these optimized models are then targeted using our SDAccel Compiler, so that they can go into the FPGA and generate a bitstream. Finally, we have our SDI Compilers, which provides bitten interfaces. And so we can integrate very easily with the Caffe and TensorFlow families.

Niyati Shah: What I would like to leave you with is that regardless of your background, whether it be software, hardware, or anything between, we at Xilinx have a tool that will allow you to take your design and run it on our FPGAs easily and efficiently. Thank you.

Eva Condron-Wells: Thank you. Our next presenter is Changyi Su.

Staff Design Engineer Changyi Su gives a talk on machine learning platforms at Xiliinx Girl Geek Dinner.

Changyi Su: Thank you for your introduction. My name is Changyi Su. I’m a design engineer from the device power and signaling technology team. My job is focusing on memory interface, timing analysis, and where a [inaudible].

Changyi Su: So today, I will go over one aspect of machine learning, the memory. So I will explain why memory is one of the enabler for machine learning and how Xilinx can be part of the solutions.

Changyi Su: We know that deep learning is one of the many approaches of machine learning, which try to simulate the human brain working with neurons. The neural network has a layered structure, each of the layers, the dot, simulate the neuron. The line between the nodes is a wait. So all the neuron network does is a computation. To compute the output of each of the node by multiplying the wait and the each of the input nodes, and then plug into the activation function. So as shown in this figure, compared to other machine learning algorithms, deep learning algorithms scale up much better with small data. Therefore, the performance of deep learning algorithm is limited by the need for a better hardware acceleration for scaling up data size and algorithm size.

Changyi Su: Recently, FPGA become a very strong competitor to GPUs, to serve as well based accelerator for machine learning. So with a programmable, flexible, how well configuration FPGA often provide better performance per watt to GPUs. Xilinx FPGAs support many types of memory technologies, either internal or external to the device. Compared to the off-chip memory, the on-chip memory has lower latency, lower power consumption, but a higher data bandwidth. FPGA devices offer the industrial leading 500 megabit on-chip memory storage space. So this allow the users to create on-chip memory of real size to suit their applications and also eliminate some of the external components. However, the on-chip memory is very expensive, and hard to expand the capacity. Therefore, the hardware accelerators still have to depend the external memory to meet the storage requirement for machine learning. And also the bandwidth of the off-chip memory is a bottleneck.

Changyi Su: So, with Xilinx’s devices, engineers are able to optimize the memory solution for different applications. For example, for machine learning, the intermediate data… Activation data is usually stored in on-chip memory, to reduce the data movement between the processor and the off-chip memory. The HBM and DDR can be used to store the input data in a right [inaudible], the way to write a parameters.

Changyi Su: So, when we’re working on the memory solution, one big challenge is the trade off between the memory and the computer resources, to achieve the best performance with the lowest latency and lowest power consumption. So, this is a most amazing part of my job in Xilinx. And this makes me a Girl Geek.

Changyi Su: As I mentioned in the previous slide, due to the capacity limitation on-chip memory, how the accelerator still have to rely on external memory to provide the massive storage for machine learning. So over the years, the DRAM Chip density is scaling up. Therefore, the DDR memory capacity upgrade is one of the easiest way to immediately improve the system performance. So we can increase the memory density by using high density DRAM chips, or multiple die package of DRAM chips. Also, the dual in-line memory model–DIMM–is very effective to increase memory capacity with minimum PCP space. So also, DIMM is a model which can turn several DRAM chips on one side or both side of a small circuit board. DIMM can be also config to a multiple RAM configuration to further increase memory capacity. However, with multiple loading, the signal integrity of the memory channel is severely degraded. So the entire system may not operate reliably at higher data rates. Fortunately, most of this can be solved by optimizing the channel configuration with the efforts and expertise from memory design system engineers.

Changyi Su: Over the past 10 years, the DDR memory data bandwidth capability did not evolve quickly enough to keep pace with the bandwidth demanding from applications such as machine learning, video transcoding. So to bridge the bandwidth gap, Xilinx introduce high bandwidth memory, HBM. HBM take advantage of circuit stacking technology that puts FPGA, DRAM, side by side in the same package. So, the whole package DRAM structure together with one thousand data bandwidth, HBM not only can provide extra more storage space, but also enable terabyte per second data bandwidth. So with HBM enabled FPGA devices, fewer DDR components are needed. For some extreme case, like this example, without any external memory components, Xilinx’s HBM solution can provide the same capacity, but much higher data bandwidth, and the better power efficiency.

Changyi Su: So the takeaway of my presentation today is evolving machine learning workloads demand varying bandwidth requirements. Xilinx’s diverse memory technologies enable it. Thank you.

Eva Condron-Wells: Thank you Changyi. And our next speaker and final lightening talk presenter is Uma Madhugiri Dayananda.

Senior Software Engineer Uma Madhugiri Dayananda gives a talk on real time video transcoding at Xilinx Girl Geek Dinner.

Uma Madhugiri Dayananda: Hello, everyone. Today we’re going to explore how real time transcoding can be accelerated on FPGA in the context of data center. Can you guys take a guess on what kinds of media applications these are?

Uma Madhugiri Dayananda: Sorry?

Audience Member: Facebook.

Uma Madhugiri Dayananda: Yeah. Any others?

Uma Madhugiri Dayananda: So, that’s Facebook Live and Twitch Live Streaming, that’s used for gaming.

Uma Madhugiri Dayananda: And, it’s just not these two applications, you also have YouTube Live, and LinkedIn just announced their live streaming application this couple of weeks ago. And it’s not just limited to these applications, live video is just everywhere and it’s growing rapidly.

Uma Madhugiri Dayananda: What’s happening behind these live videos? We have a huge distribution of clients here for example, our cellphones, the tablets, the PC, the TVs, are connected all via wireless networks and each of them have their own resolution and network characteristics. And we want to download the live video to each of these clients, so… To download the live video to each of these clients, the live video input is pre-encoded with the HEVC Encoder, at a different resolution and a bitrate of… And using video transcoding. So, essentially video transcoding is the conversion of one video encoding format into another.

Uma Madhugiri Dayananda: What is the advantage of video transcoding? It provides savings in terms of bandwidth and storage [inaudible]. So how many of you have seen live videos then? You experienced video stars by watching live video? Video consumes a lot of processing resources and power, and for live video applications, latencies involves milliseconds, so…

Uma Madhugiri Dayananda: Here’s a plot that shows the encoder quality preset versus the performance. I’m taking a specific example of x265 preset slow, with the quality preset on it’s supposed to be very good, like when you look at it visually. So, comparing that… If it’s encoded with CPU, you get 10 frames per second, but if the same application is being run on FPGA it’s like 120 frames per second. And for data centered context, it’s not just the quality and performance, you also have to consider the power as well. So considering all the three in the equation, you can see that it is like 72x acceleration.

Uma Madhugiri Dayananda: How does the FPGA solution compare to GPU solution? So here is an example of FPGA based HEVC Encoder compared against NVIDIA GPU. HEVC Encoder and see at the same quality level there’s 35% bitrate savings. Which translates into your bandwidth and streaming cost reduction.

Uma Madhugiri Dayananda: Another reason for using FPGA is probably your transcoding. So the video codec world is changing with the introduction of new codecs every few years. If you the timeline, from 2010 to 2020, there’s four new codecs. We already have HEVC and VP9, and AV1 was just standardized last year, and VVC is going to be standardized next year, so… You have a hardware or a custom chip for each of these codec, if you have that then it’s going to be a lengthy design process and also you can’t use the hardware, so… If you use FPGA, then the applications can be adapted… On the same FPGA you have HEVC application be running and also the VP9 application, so FPGAs are adaptable and reusable.

Uma Madhugiri Dayananda: What is happening behind the scenes? How is the video transcoding happening on the FPGA? We have the live video coming in from the host’s CPU and that’s being decoded on the FPGA using H.264 Decoder, and scaled into multiple resolution using adaptive [inaudible] scaler. And these scaled resolution videos are again to be encoded with the better quality encoder, and sent out back to the host.

Uma Madhugiri Dayananda: Here is this video transcoding stack that Xilinx offers. I work end to end on this pipeline. Building FFmpeg applications, XMA plug-ins, testing these applications on different SDAccel boards, targeting different devices. And not just these, I also work on video algorithms, including the quality, for improving the quality, and benchmarking encoders from partners according to customer requirements, so… I guess that makes me a Girl Geek. I work on things that I’m passionate about, video compression technologies.

Uma Madhugiri Dayananda: What I would like you to take away from this talk is FPGAs give you better performance, better adaptable and reusable, and you don’t necessarily have to be a hardware engineer to use FPGA, you can be a software engineer and still use FPGA to isolate your application. Thank you.

Eva Condron-Wells: Thank you so much, Uma, and to all of our lightening talk presenters. Let’s shift gears to our panel of senior Xilinx leaders. Here to lead our panel discussion is Tom Wurtz, Senior Director of Documentation and Program Management. And our distinguished panel.

Tom Wurtz: So tonight we’re going to explore the topic of acceleration. We’ve got a great panel of senior Xilinx leaders here to enjoy the [inaudible] discussion. So we’re going to start with Jayashree Rangarajan. She’s a Senior Director of Software Development. And her Girl Geek power is simplifying solutions to complex engineering ideas. We’ve also got Lori Pouqeutte, who’s our Vice President of Global Customer Operations, and her Girl Geek power is knowing what the customer wants and needs before they know what they want and need. All right. Next up is Jennifer Wong, and she is a Vice President of FPGA Product Development, and her Girl Geek power is optimizing results for both engineering and management. And finally, we have Ambs Kesavan, who’s our Senior Director of Software Infrastructure, Engineering & DevOps, and her Girl Geek power is improving the development efficiency of using tools both in the cloud and on something.

Tom Wurtz: All right, so let’s talk about this acceleration, that’s a pretty wide topic. We’re going to take it in a couple of different directions. First, computational acceleration. And then we’re going to make it a little bit more personal and talk about careers, as well as teamwork. So, we’re going to start with the hard stuff. So we’re going to geek out a little bit on computational acceleration. You heard Changyi talk earlier about machine learning. So this is things like image classification, motion detect, and speech recognition. So Ambs, I’m going to have you go first, and I’m going to have you talk us through some of the bottlenecks and challenges in this.

Senior Director of Software Infrastructure Engineering and DevOps speaking at Xilinx Girl Geek Dinner.

Ambs Kesavan: Thanks, Tom. I actually view this as an opportunity rather than a challenge. So, I’ll talk about the opportunities here and I’ll explain why I view that way. So we are in an era of big data, and there is a lot of statistics about big data. And I was looking at a recent article that said every single second, we generate about terabytes of data from connected devices and sensors around us, and 70% of this data is video. And that amounts to about 800 million hours every single day or something like that, and businesses are trying to take advantage of this data. They want to mine the data, to be able to look through things. One is for better customer service and also in [inaudible].

Ambs Kesavan: So machine learning applications are getting innovated at that rapid pace, in every single industrial segment, whether it is retail, or finance, or healthcare, Uma talked about video transcoding, speech recognition, name it. Every single industry is going through innovation. And these machine learning applications, they actually have the most algorithms for actually doing the machine learning. And these algorithms, if you run on CPU, that is no longer sufficient. It is not a scalable given the massive volume of data that we are looking at.

Ambs Kesavan: So acceleration is the way to go. And innovation needs to happen both in hardware, also in software, in order to accelerate this machine learning applications and algorithms. And that’s the opportunity, Tom. And Xilinx is well positioned Tom, [inaudible].

Tom Wurtz: Thanks. Lori, maybe you can join in with a few more thoughts.

VP of Global Customer Operations Lori Pouquette talks about opportunities and challenges in supply chain for applying machine learning to the business at Xilinx Girl Geek Dinner.

Lori Pouquette: Sure, and I’ll take this more from the practical application in business. So we’re building a lot of capabilities to accelerate machine learning, but we actually have to apply that too in our business. And in supply chain… There’s quite a bit of opportunity in supply chain for applying machine learning to the business. And it provides good ROI to the business. A couple of the areas that we’re looking at are, what I call predictability or or predictable analytics. And one of the applications would be being able to understand the profile of the die that is coming out of the fab very early on, so you can actually match that to the demand much earlier in the work stream and optimize the use of your materials. Another predictable application is actually in the back end, when you talk about equipment. One of the things that shuts us down is unplanned machine downtime. So if we can anticipate and understand the profiles of the machinery so that we prevent it from going down in the first place, it’ll definitely propel the business. The challenges with all of this, though, are the massive amount of data that you have to gather to really train your models and make sure you have the right algorithms, so you get the ROI out of it.

Tom Wurtz: So there’s clearly a lot of opportunity is this space, so… You see companies like Google and Intel, they’re doing these dedicated AI chips, there’s dozens of startups that are actually going down this path as well. And maybe Jayashree, you can walk us through kind of what you see in terms of where is the market going from here.

Jayashree Ranga: You all heard Ambs talk about the terabytes of data getting generated. And there’s also… She was mentioning about algorithms that need to be developed… And specifically, if you look at the computing today, CPUs generally are meant for general purpose computing. When you talk of AI, and machine learning in particular, you have two types of learning. There is training, and then there is inference, right. And then training, you use all of this data that you have accumulated and we are training certain models to do a particular task, [inaudible] domain, or [inaudible] exploration, or whatever. But then, when you have to deploy that model, actually for doing the inferencing, you want this to be done in such as way that the computation happens a lot faster and the CPUs are not scaling… You probably have heard at many conferences that [inaudible] are not scaling anymore.

Jayashree Ranga: So there is a need for us to be looking at how can we accelerate the solutions that are targeted for these specific applications. So, now you’re seeing companies looking for like… Especially the examples that, Tom you gave about the GPUs and the… That’s primarily because they saw a need for how do I accelerate this? I can’t do it just with software, I need to be building custom hardware. And, it doesn’t just stop the building that accelerator, but you’ll need to have some surrounding support in the system because data that needs to come into that accelerator, and how’s the communication going to happen? So there is a need for these specialized architectures to be built and that’s why are seeing a lot of these startups getting funded to find that next big accelerator architecture that we can build.

Jayashree Ranga: Second thing with machine learning also is you probably are hearing new networks getting created everyday, right. Which means you want an architecture that you don’t build for one network, but two years later you’re not able to use it. So, you want to a hardware architecture that is scalable with the needs. And that’s an area where Xilinx’s provided solution, which is adaptable and reusable, you saw it in the presentations earlier, it provides solutions that people can use for building networks that gets scaled with new models that come in. Especially with the startups that you’re talking about, I’m waiting to see as well, which ones succeed, which ones get taken over.

Tom Wurtz: Yeah. So adaptability, programmability, is part of the Xilinx DNA. So, when we think about programmability, there’s the idea of buying a chip off the shelf and programming it to do what you want. But there’s also the notion of, once that chip is actually in operation, being able to turn off part of it, and reprogram just that part to take on a different workload. And it’s part of the general philosophy of Xilinx, and as we look forward to the next generation of chips, it becomes even more impressive what we’re planning to do, so… Jennifer, maybe you can walk us through what Versal looks like.

Jennifer Wong: Earlier I see a lot of hands go up when we ask “Who knows about Xilinx?” So I saw a lot of hands go up. Then I heard, “How many people are hardware engineers here?” So I do see quite a few hands go up. So I wouldn’t be surprised that some of you here are some of our customers, or you have used our products, it depends… When you were in school… So you must be very familiar with our earlier product lines like UltraScale or UltraScale Plus.

Jennifer Wong: So Versal, it’s a significant step up of UltraScale, UltraScale Plus in terms of performance. So what is Versal? Versal is our first 7 nanometer product, TSMC’s latest process node, and is the industry’s first ACAP. ACAP stands for adaptive compute acceleration platform. But this is truly a platform device, not your old FPGAs. Though I should say this is the really revolutionary architecture. So this revolutionary architecture combines a scalar engine, an adaptive engine, an intelligent engine, to give us this significant performance improvement. The performance improvement can go up to about 20x of today’s GPU, or 100x up to today’s CPUs.

Jennifer Wong: So, now , how do we achieve this kind of a performance improvement. It’s pretty impressive. So, given today’s focus topic is machine learning and compute acceleration, I’m going to talk a little bit about the intelligent engine. Intelligent engine is also known as AI engine. Internally, we have a name called AI engine. So this is specially designed for compute intensive applications, like machine learning and wireless operations. Now go to the next level, what is AI engine made up of? It is really a wide array of integrated DSP engines, which are capable of [inaudible] and complex MAC operations. Now we have all these very very powerful engines. We need to think about how to connect them together in order to take advantage of them in terms of hardware acceleration.

Jennifer Wong: So you have used our products before, you must know MPSoCs. So, in MPSoCs we have a processor subsystem sitting alongside with the FPGA fabric. And these two entities are sitting side by side with some interface in between them, but the bandwidth is relatively limited. So, when they operate, they are operating fairly independently. So the difference between the older generation products and Versal is we added a very powerful NoC engine in between all these powerful engines. NoC stands for network on chip. This is not new in the industry, so NoC is very standard in ASIC. But what we are doing here is applying it to our architecture in order for us to leverage their compute acceleration.

Jennifer Wong: So what is compute acceleration, or hardware acceleration? It is… What it is a design when you can partition the area that are very very performance critical. So you partition it out, and put it into these powerful engines, and use compute acceleration to make the performance improve. And then, after the partition, you can have the slower function continue to run on the processor’s subsystem. And that’s how you achieve the big performance gain. And I’m going to stop right here, there are other innovations in Versal architecture. I’ll be happy to talk to you, if you’re interested, later this evening.

Tom Wurtz: Thanks, Jennifer. Versal is definitely an example of technology escalating at an incredible pace. So I’m going to ask each of you to kind of give some thoughts on where you think the things are going to go in the next three to five years in the acceleration space.

Jennifer Wong: Maybe I’ll just follow up on what I just said. So today’s product, what we have today is pretty big. If you look at our die size, it’s huge, and power is pretty high. So, they’re going to data center, into the cloud. And I see this intensive computation not stopping, because this is in the very early stage and I see that continue to go. But in three or five years, as our process becomes more mature, our technology become more advanced, I see more and more functions going to the edge devices, like mobile phones in your hands today. And I think in future, that’s where it will go.

Ambs Kesavan: So, Jennifer let me add a slightly different viewpoint. We talk quite a bit about computer acceleration. Even tn the presentations we heard about computer acceleration, and here what happens is you’re transferring massive amounts of data from storage to compute, doing that acceleration. And then transferring the results back to storage, and there is lot of data movement happening back and forth. And that’s not necessarily very efficient, you’ll run into [inaudible] storage, and networking. And every single data center, whether it is cloud data center or on ground data center, you’re going to have compute, storage, and networking. So, what if you do the computation closer to storage? So you’re actually doing the acceleration closer to storage instead of doing the data transfer back and forth. And that’s the area Xilinx is innovating and that’s… One example that I can give is the smart SSD announcement, that happened couple of months ago, when Samsung had it’s tech day. And that precisely is doing acceleration at the storage itself. And there is also similar innovation happening on the networking with SmartLynq. So it essentially, it’s converge solution with computer acceleration, storage acceleration, and networking acceleration, and that’s where the industry, I think, will benefit a lot.

Tom Wurtz: Maybe you could talk about the software a little bit, Jayashree.

Senior Director of Software Development Jayashree Rangarajan speaks on a panel discussion at Xilinx Girl Geek Dinner.

Jayashree Ranga: So, if you look at where a lot of this machine learning development is happening, it’s happening on the cloud. And they are the software developers. Throughout [inaudible] Niyati’s presentation, data scientists are looking at these massive amounts of data and looking at how am I going to write the right algorithm to solve this problem, right, and they are operating at higher levels. So what I see happening with the software is many layers of libraries being built, where these are libraries probably optimized to work for the hardware architecture that we’re targeting. Not necessarily done by the data scientists, but it’s provided by the company that is also delivering the hardware. Plus, there’s probably going to be AI specific library. For instance, we talked about video transcoding. So if you have open CV libraries that need to be provided for you. If your are software operator, you will understand this, right. Because we don’t… Anymore write string compares and stuff. You use [inaudible] libraries or SDL. So you are going to see stacks of libraries built, which are the highest level the application developer… Whether they are in a Caffe framework or a FFmpeg framework if they’re dong video transcoding. They’re going to leverage these libraries.

Jayashree Ranga: So I see a lot of innovations happening in that realm. And companies will be providing their own libraries. I see open source development or certain APIs that can be leveraged by people who are trying to address a lot of machine learning problems and various [inaudible].

Lori Pouquette: And then, if you take that from the engineering world to “Where is this all going to be used?” Xilinx has long been serving multiple end market segments, from automotive, to communications, tested measurement, medical, but our new focus now is on the data center area. The proliferation of the data, the video, it’s all going to be needing to be stored up in the cloud or on the premises. So we’ve really now got this strong focus on data center. And as part of that, in addition to selling our semiconductor devices, we’ve also started to sell boards. So we have Alveo product, which you heard Eva talk about earlier, you can see it in the demo room. So this is now making the acceleration capability, from just the semiconductor to enabling the customer with a board. So they can use the board and then go on to their design work for their entire solution much faster. They don’t even have to just start with the FPGA.

Lori Pouquette: Now, as we go out into time, what’s going to be really good for everybody is, we’re still serving all these other in markets, and as in markets like automotive really get into advanced autonomous drive or advanced applications, they’re all going to need to store that information somewhere. And they’re all going to need to process that information, and they may need to do that at the edge very rapidly, or they may be able to do that in a central place. But, basically what we’re doing here with acceleration isn’t just going to serve the base acceleration market, but all of our markets.

Tom Wurtz: Well we got pretty hard there to the extent that we pretty much used up most of our time. But I do want to take one moment to ask each of your, if you were to put into one single word, what does it take to actually accelerate a team, what would that be? And let’s start with you, Ambs.

Ambs Kesavan: Communication.

Jennifer Wong: Teamwork for me.

Lori Pouquette: Focus.

Jayashree Ranga: I’m going to go with two words, I would say it’s the winning attitude.

Tom Wurtz: All right. Thank you very much for all of our panelists.

Eva Condron-Wells: Thank you so much to our panelists and to our lightening talk presenters. At this time we’d like to open it up to the floor. And I’ll need to steal a microphone from one of our panelists, so we’ll share. Pardon me. Thank you.

Eva Condron-Wells: So, you’ve heard a lot of different insights, different perspectives. We’d like to hear a few questions. We probably have time for… We’ll take three. So… And we’ll continue the conversation afterwards. So if you’re still burning… You have a burning question in your mind, please know that we are planning to be here until 9:00, and we have plenty of demos to share too. So… And don’t forget to take your gift on your way out, I want to say that before you mentally leave… While I have your attention, that we have a gift for all of you, that we would like you to take on your way out, in the lobby of when you came in. So, please be sure to take that with you.

Eva Condron-Wells: So, that said, I’ve given you a little bit of time to think about your question, and I have a microphone right here. Come on up, let’s have… If you don’t mind saying your name and your question to our panel.

Sara Biyabani: Sara Biyabani, GridComm CTO. So the question I have, for engineering… We talked about acceleration, so I want to start with FPGA is a [inaudible] processors, you know, they’re part of the screen, right? They’re not the… Well, I mean they could be the star, they could be the diva. So then there’s the processor, right, and you’re not going in the space, competing with x86 or R. So what does that architecture look like? What’s your ideal architecture?

Jennifer Wong: I think we have a pretty good… We do have a processor. So we have a… So for the Versal we have the A72 cores, we a have a dual A72 core in the processor subsystem, in the scalar engine, what we call the scalar engine these two processors, subsystem, you a two A72 cores and then two R5 cores in there. So from a processing standpoint, we do have capability of doing that. And now we added a lot of these architecture that we can accelerate functions. So that is what we think were our niches. So we can partition it where very critical functions, we can put it into the more expensive side. We use a lot of silicon area for acceleration hardware. So we would smartly do that, and say, okay, “What is the important one that we can partition out, put it into hardware acceleration, and leave the processor still running?” So that is what we think where the niche. Everything is on one piece of silicon. So think about it, if you’re doing it outside, you have processors and other components. So, the interface takes a very long time. The key is integration for us. Everywhere, the smaller the footprint, the more integration you do, the more performance you… Both performance and power. So, whenever you got our chip, power is a big deal. So, performance and power both are advantage to integration. And that’s where we think we going in future.

Eva Condron-Wells: Thank you, Jennifer. We have actually three questions just popped up, right all in the same area. So we’re going with that energy, and please, your name and your question.

Sylvita: Hi, I’m Sylvita. First of all, thank you to Xilinx for hosting this event. Given all the other Girl Geek Dinners I’ve been, it’s nice to see somebody on the hardware chip design side kind of take a lead here. So my question is related to that. Because most of the discussion in the big data, enterprise SaaS, or AI has been around the software and the algorithms. The first time I saw a focus on hardware was actually a Startup Grind where one of the presenters talked about , don’t write off the chip guys here, because there’s a next revolution coming where we’re going to see a lot of custom built chips for AI applications. And given that we’re talking about… First of all, this is a Girl Geek Dinner, and given that we’re talking about the fact that we need women to be on an equal footing in the way that the AI is going to evolve, what are some of the ideas you might have for some of the younger folks to continue in this space?

Jayashree Ranga: Mic check? Can you hear me okay?

Jayashree Ranga: I can think of a couple of things, right. Computer architecture, ’cause as I was talking earlier about, hey there’s many architectures that need to… They’re probably waiting to be innovated, right, because we are talking of machine learning in many many domains and stuff. So I do think, as young women who are looking to hey, what do I want to do if I want to enter into hardware, I think having a good grounding in computer architecture principles is going to go a long way. And just learning the hardware aspect, alone, is not going to be sufficient. You also need to understand which domain you’re targeting. So you need to know the end customer that you’re going to be influencing. So you need to learn about the software component, also. So I think, if you want to excel in both sides, as you are getting your early education, having good grounding in both hardware principles as well as software programming principles help you better understand the needs on both sides. But as you go further, and you are looking to specialize, then at that point, it’s an individual style choice as to whether hardware attracts you more or software attracts you more. But I think it’s good to keep your options open.

Eva Condron-Wells: Great. Thank you so much. We’ll take two more questions, and then continue with our networking.

Mung: Okay, and my name is Mung. I’m a software engineer working hardware design company. You already addressed a little bit about that, and I just wonder if you can address a little bit more regarding into FPGA application in machine learning and hardware acceleration to win over ASIC in that field.

VP of FPGA Product Development Jennifer Wong speaking at Xilinx Girl Geek Dinner.

Jennifer Wong: I think the jury is still out, but I think everybody is working very hard. This is a very hard space. And I think everybody is trying a very different route. And Xilinx has Xilinx’s niche. And I think what we give here… What our specialty is, is we are reconfigurable. Aside from being able to partition… That’s definitely a big deal, we can allow software to run on software, hardware fabric to run on past hardware… The bigger part, I think, is the reconfiguring part, which I didn’t talk about earlier. So we talked about many many different workloads today. During the day, the data center can be running one kind of work load, in the evening it’s a different workload. So, what we excel, here, is we allow reconfiguration. So even you can… Within the ACAP, you can run different workloads at different times, with the exact same piece of hardware. So we believe that is a very big niche that we can further leverage in this particular space.

Eva Condron-Wells: Thank you.

Lori Pouquette: I just want to add that, beside the reconfigurability, there’s the costs. So the costs of doing ASIC is becoming prohibitive for many applications. So on higher volume applications it’s still an option. But many of our customers who might’ve classically done ASICS, are moving away from it because it’s just not making financial sense. So there’s that piece of it too.

Eva Condron-Wells: Thank you, Lori. And our final question.

Wolfgang: Hello, my name is Wolfgang. I’m a hardware engineer. And I want to thank you that I have the chance to ask a question even though I am not a Girl Geek. So, my question is about reliability. When we have Ultra high speed computations in applications such as control of industrial processes or autonomous driving, it is not only imperative that those computations are very fast, but the results need to be very reliable. And we don’t always have the option to just [inaudible] processing memory because we have a sensor somewhere, a camera, it sends data to some processor, and that processor has to make a decision and send it to some machine that needs to act and do the right thing. So what are your thoughts about reliability, or maybe a hint, it has to do with something with high speed interfaces and a robustness against big errors, but there are also many other aspects to that.

Eva Condron-Wells: Thank you.

Jennifer Wong: Okay, so reliability has always been a big issue for us. In the past, what we do is we allow [inaudible] reliability and we do things very carefully with our foundry. Because foundry give us some specs that we have to follow through. TSMC is pretty well known in terms of it being rather conservative. So, they give us specs and we obviously negotiate with them. There is a little but of push you can do, because everybody is competing for performance and power. The more you can push the foundry, the more you can gain the advantage. So, this is maybe against what you are asking, but what I’m trying to say here is we do a very [inaudible] balancing act in terms of balance versus reliability. We don’t completely ignore reliability. We go for performance, but we always make sure we can meet our reliability. And we do a very very thorough quality QA in the end. And, also, we have qualification… Pretty substantial qualification… Different time, maybe you can add to it.

Lori Pouquette: Yes. So on the quality side, we have many in markets we serve. So we have commercial grade, we have automotive grade, and we even have an aerospace… So all of those have different levels of quality and reliability qualifications that we go through. So we definitely are very attuned to the upcoming challenges of the technology to be able to respond quickly in those types of situations you’re describing. But we definitely offer a variety, and do quite a bit of different qualifications depending on the markets that we’re serving.

Eva Condron-Wells: All right. Thank you all so much for your very thoughtful questions. And thank you all, our guests, our speakers, panelists, for your insights. We genuinely appreciate you taking the time to share your insights with this team and group. So, that said, we are officially closing our technical talk. But we are not done with our evening yet, and we are happy to stick around and answer more questions. Some of our Xilinx employees are wearing “ask me about” stickers, you’re welcome to engage with them. I will be wearing one too, so find me later. And of course, we have our dessert, and networking, and demos. Thank you all so much. This concludes the technical talk, thank you.

Applause from the girl geeks at Xilinx Girl Geek Dinner after the panel discussion. Thanks for joining us in San Jose!

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Hundreds of girl geeks came to Stitch Fix Girl Geek Dinner for the food, drinks, good company and excellent talks from CTO Cathy Polinsky to Principal Software Engineer Erin Dees.

Speakers:
Cathy Polinsky / CTO / Stitch Fix
Emma Colner / Software Engineer / Stitch Fix
Lila Bowker / Product Manager, Engagement / Stitch Fix
Anna Schneider / Data Science Manager / Stitch Fix
Erin Boyle / Data Scientist / Stitch Fix
Bingrui Tang / Senior UX Designer / Stitch Fix
Erin Dees / Principal Software Engineer / Stitch Fix
Angie Chang / CEO & Founder / Girl Geek X
Gretchen DeKnikker / COO / Girl Geek X
Sukrutha Bhadouria / CTO & Co-Founder / Girl Geek X

Transcript of Stitch Fix Girl Geek Dinner – Lightning Talks:

Angie Chang: Hello! Awesome. Thanks everyone for coming out to Stitch Fix tonight. My name’s Angie Chang, I’m the founder of Girl Geek X. It’s been 11 years of hosting weekly dinners at companies around the Silicon Valley in San Francisco. I want to thank you so much for continuing to come back, and also if it’s your first time, I hope you’ve met some amazing people. I learn something new every time, when I come to another one of these events. This is Stitch Fix’s second time hosting this event, and it’s really great seeing them grow, and I’m really excited to hear them speak again.

Gretchen DeKnikker: Hey, I’m Gretchen. How many of you guys are wearing like your Stitch Fix stuff today? Yes. I made sure that I had on all my stuff, and I feel really cute, and I have to say you guys look extra cute. Like you always look cute at the events, but you look extra cute tonight. I can tell. Okay, so how many people is this your first time? Whoa, a lot. Okay. So we actually do these every single week, so make sure you’re on the mailing list, and the next one’s at Cisco, and there’s a few more in the city, Intuit, like there’s a whole bunch of really awesome ones coming up.

Gretchen DeKnikker: We do have a podcast also, so find that on your favorite station. We just did one on uncommon unconventional career journeys, which is a fun topic. But we do mentorship, imposter syndrome, like all of these, so check it out and give us some feedback. Read it, tell us what you’d like to hear about. Tell us how we can be better. And Sukrutha.

Sukrutha Bhadouria: Thanks. Hi everyone, I actually did try wear my Stitch Fix pants, but they don’t fit anymore.

Speaker: We have maternity.

Sukrutha Bhadouria: You do, you have maternity? Okay. I did not get that notification. Thank you. No, but, anyway, the clothes are really cute and fun, and the last time when Stitch Fix sponsored, they spoke about how they used data to correctly identify the styles and the sizing that you need, and it just really blew my mind, how you think, “Oh, I’m just getting clothes in the mail.” But there’s so much thought and engineering and data that goes into it that shouldn’t go unnoticed.

Sukrutha Bhadouria: Speaking of data, we’re also trying to get a sense, so if it’s your first time, how many of you are going to sign up for our mailing list? That’s-

Gretchen DeKnikker: You can decide later. Like we haven’t proven anything, other than that they can get you good food.

Sukrutha Bhadouria: Yeah, but I do want to say it’s really easy to sign up for our mailing list. Go to girlgeek.io, and there’s like one click and you’re in. I do want to say Cathy is my absolute inspiration. We do podcasts, and we also do virtual conferences, and the first time we did a virtual conference she was kind enough to agree to kick it off and do the keynote. And so even that audio and video is available on our website, you can easily access it, and also on our YouTube.

Sukrutha Bhadouria: So enough from me, because there are great talks coming up, and Cathy is going to kick it off. I know Cathy, because she used to be at Salesforce, kicking butt there. And she’s kicking butt here. I’m going to hand it off to you, Cathy. Thank you so much for hosting again.

CTO Cathy Polinsky talks about the company’s product, the people, and partnership at Stitch Fix Girl Geek Dinner.

Cathy Polinsky: Thanks. Thank you guys. Great. Thanks for giving us the opportunity to host. I really appreciate the organization, Geek Girl X, and the mission of really bringing together women from all over the Bay Area to connect, and to network, and to share our experiences. So thank you all for coming here, and especially welcome to people who are coming to their first Geek Girl event.

Cathy Polinsky: How many of you have never used Stitch Fix before? Yay, awesome. Well, you all have gift certificates, and so hopefully you’ll give us a try. But Stitch Fix is an online personal styling service, so we’re disrupting how people can find things that they love, and our mission is to help people feel and look their best. The way that we do this is we pair human stylists with data science to help people discover their looks.

Cathy Polinsky: And so, I mean many of you have tried to buy clothes online, it’s a pretty miserable experience, and what we’re doing is first you fill out a style profile, similar to a dating profile, but about things that you like to wear. We then use dozens of machine learning algorithms to get match scores against all the items in our inventory. We send those match scores to our stylists, and we have over 4,000 stylists who work for us.

Cathy Polinsky: These are employees of ours. We feel really passionate about not replicating the Uber gig economy of contractors, but really these are employees of ours who work from home, and are really passionate about serving our client’s needs. They hand curate a Fix just for you. They pick the clothes, they send it to one of our fulfillment centers. We put it up in a nice box, we send it to your house. You get to try it on at home. You keep what you like, you send back the rest. We pay for shipping both ways.

Cathy Polinsky: The great thing about our model is that we get better and better the more we get to know you, and you share data about what you like, and what you don’t like, and that gets fed back into our model. And so we’re an eight-year-old company, and proud that Fast Company named us one of the most innovative companies last year. Woo! Thank you.

Cathy Polinsky: This is Katrina Lake, our CEO and founder, and I feel really passionate about working for a female led company. She is a really amazing entrepreneur and leader, that I really get so excited every day to get to work with her. It’s a very data driven company, led by a very data driven leader. That really goes into the innovation of what we do.

Cathy Polinsky: We’re eight years old, we’re a newly public company, making over a billion dollars in revenue. We’re profitable. We’ve got business lines for women’s, for men’s, for kids, for plus, maternity. We’re just about to launch our first international company in the UK, so look for the announcement soon. If you know anyone in the UK, let them know once it’s launched and we’d love to have them try out our service.

Cathy Polinsky: I really get excited about our business model, and it’s just very interesting to deal with something that has a tangible product, and a huge operational aspect. I’ve worked at companies like Salesforce and Yahoo and Amazon, and this is just a different aspect of how I’m working with technology, with our tech teams.

Cathy Polinsky: It’s also really amazing what a strong engineering culture we have, and company culture. We have what we call the Stitch Fix OS, it’s our operating system for how our teams work together. And I think it’s really great to be able to work for a company where you can see the values that you have, aligned with how we run ourselves as a company.

Cathy Polinsky: Two things that really stand out to me as a technologist, is one is our value around authenticity. When I started my career as a software engineer all I wanted to do was fit in and look like one of the guys. I didn’t want to stand out as a woman. I wore baggy T-shirts and jeans, and just wanted to be treated as a great software engineer, and to be respected for what I did, rather than stand out as someone who is different.

Cathy Polinsky: There’s a lot of overload that goes in your brain to just try to fit in, to just try to fit in the same box, talk like one of the guys, to play the first person shooter games, or whatever it is that we were doing back then. It is a little exhausting to think about trying to fit in, instead of just getting to work, focus on your work.

Cathy Polinsky: Stitch Fix is a company where we are trying to help people look and feel their best, because we feel like that they can go out and lead more confident lives, and just the feeling you have when you’re wearing that great outfit and being able to be yourself really matters. We also have a mission for our employees to do their best work, and a lot of that comes down to being their authentic self. So not having to feel like they have to double check their email three times, or phrase things to seem less emotional, or more powerful, or whatever codified words that it might mean to kind of fit into that mold.

Cathy Polinsky: And so we have authenticity as one of our Stitch Fix values, of letting everybody be themselves, and thinking about the culture additive that brings to the organization, so that people can focus on their work instead of just trying to fit into the mold.

Cathy Polinsky: Then the other thing that we have a really strong value is around partnership, so it’s a complex business model that we have. First it starts with the merchandise. We own all of the inventory in our system, that we’re selling to our clients, and if we don’t buy the right inventory, and the right quantities, we’ll never serve our client’s needs. Then we’ve got this huge workforce of stylists and making sure that they have all the tools that they need to operate at their job every day.

Cathy Polinsky: Then we have a huge operational aspect of how we get those products to the clients in the right way, and manage all of our inventory and our costs. And then we have this whole business with our website, the style profile, and engaging products that want you to come back to our site every day, so that we can get to know you better and better.

Cathy Polinsky: If you think about all those aspects, there’s … I’m always surprised by how little changes on one side of those have deep impact on other sides. We could make some changes to how many items we send in a Fix, and that could change the inventory allocation that we have available for the next person. And so it is one of the most partnership driven companies that I have worked for, in that you really have to think about not just your own area, but how that could have an impact across the company.

Cathy Polinsky: We thought what’s really interesting about our business is how strong of an EQ we have here at Stitch Fix, and how that has really led to our innovation and success as a company. And that we really strive for that when we’re hiring technologists, so people who can think not just about building something to spec, but really thinking about understanding the business model and how they can work together across different lines. Whether it’s a data scientist working with an engineer, or someone of the design team working with our marketing team.

Cathy Polinsky: And so we’ve got a theme today of partnership that we’d like to share with you. Talking a lot about some of the interesting projects, but really leaning into how you can use that aspect of your skills to really be a big success. I’d say one of the things that I learned about partnership came from a big misstep, I would say, as an early engineering manager. So going back to this feeling of authenticity, you only know what you see, and so being in an organization where I saw mostly male leaders, I tried to emulate a lot of them in my leadership style. It didn’t always feel comfortable, but you just try to do the things that you’ve seen to be successful when you run into tough trouble.

Cathy Polinsky: A few companies ago I was working on a project, and my team was getting pulled in different directions, and I felt like, okay, I’ve seen the way that guys handle this, and they pound on the desk, and they really fight for their teams to make sure that they’re not being jerked around, and that they’re getting the staffing and the support that they need. And so I tried to do the same, and I pounded on the desk, and I yelled in meetings, and I said that this was just really unacceptable for how we could get something done.

Cathy Polinsky: And it didn’t work, and I kind of failed miserably at making the changes that were really needed to work on this project in a way that was getting clarity on the architecture and the designs needed at a large scale. It was like, I don’t understand why this isn’t working, it works for other people, I’ve seen managers do that in the past. But I have to say, I had some self-reflection, it didn’t feel good to yell in meetings. It wasn’t successful and it wasn’t me, and it just made everybody really miserable in the process.

Cathy Polinsky: And so the next time I went to a new company, I was like, okay, never again, I’m not going to try to be someone I’m not, and to try to get through this with anger and yelling. I’d say that I learned a lot in my next role, of really starting to build relationships. So how can I build relationships upfront, build trust, so that when we have difficult situations, instead of it getting to a point of anger it came to a conversation. I found that I really developed my leadership style, because I leaned more into my authentic self and led into building partnerships. Because I think that when you have those partnerships, you can get a lot more done.

Cathy Polinsky: I see that here every day at Stitch Fix and hope you’ll see some great learnings around partnerships that we have here today with some of our speakers. So without further ado, our theme is around technology and partnership, and we’re going to pass it off to Emma. Yeah, Emma, come on up. Where’d she go? There.

Software Engineer Emma Colner gives a talk on “Mind The Gap: How Our Brains Fool Us into Thinking We Understand” at Stitch Fix Girl Geek Dinner.

Emma Colner: I don’t know if you want your wine that’s up here, Cathy. All right. Hi everybody, my name is Emma Colner. I’m an engineer here at Stitch Fix, and I work on expert use systems. That means I build tools for my co-workers that help them do their jobs more efficiently. But in a previous life I was also a former experimental psychologist, so I really enjoy thinking about thinking, and today I’m going to be talking about how experts think, and how that differs from how novices think, and the different implications that can have for when we are collaborating together at work. Oh.

Cathy Polinsky: I stole this.

Emma Colner: Thank you. I only have two hands. So I’m just going to hold this. Okay. Like Cathy was saying, we value partnership a lot at Stitch Fix, and I think it makes a lot of sense to really try and understand how we can bridge this gap between how experts think and how novices think, and what they know. But also it’s a great opportunity for us to harness each individual person’s expertise. In that way we can kind of learn and grow together as a company.

Emma Colner: To start, I’d like you to think about someone that you really admire, someone whose skills and knowledge you really look up to, and someone that you might want to emulate one day. Imagine that this building here on the left is a representation of all that person’s knowledge about a certain topic. Now, if you were to learn how to do what this person does, how would you do it?

Emma Colner: Well, you could try and just copy what you see, but you could also … You’re not even sure of whether the building is going to be structurally sound, so what we don’t see is that this building started out as an idea, and a series of discussions, and lots of back and forth plans before there was even a foundation built. What I’m trying to say is that basically when we look at an expert we don’t see the path that they took to get to where they are.

Emma Colner: We don’t see all of the hypothesis that they tried and tested. You don’t see all the doubt that they experienced, so it’s just important to keep in mind. Part of what makes being a good partner a challenge, is that it’s impossible to truly know the experience of someone else. We can be excellent observers, and we can infer a lot about someone just by looking at them, but we don’t know everything. Part of the difficulty is that as people we naturally just fill in the blanks. When we don’t know something, we infer based on stuff that we already know.

Emma Colner: In some situations this can lead to some false understanding, so to succeed at partnership we need to work hard to bridge that gap between minds. I included this quote here on the right from Domain Driven Design, by Eric Evans, just because I thought it was relevant to what I’m talking about today. I’m not going to be talking much more about it, but I’m still in the middle of reading it, but it was a really good introduction. I recommend it.

Emma Colner: For now, I’m going to be describing three different scenarios that I have experienced in my software engineering career, and what they can teach us about our brain’s natural limitations, so we can become better communicators, problem solvers, and business partners.

Emma Colner: In scenario one I’m a junior software engineer, I’ve just started at Stitch Fix, and I have a mentor who’s a principal engineer. We pair pretty much every day, and half the time I don’t know what he’s talking about. He’s speaking in a different language, and also I don’t even know how to phrase the questions that I’m trying to ask. I don’t know how to phrase the search terms that I want to put in Google to understand what I’m trying to do. So that’s the first scenario.

Emma Colner: Second scenario is, in this scenario I’m the expert. I built something, I built a new feature, I’m trying to get it to work, but there’s a bug that I just can’t figure out. I’ve spent many hours on this, and finally I decide to ask someone for help. But then as soon as I explain the issue to that person, the answer just kind of pops out at me, and the other person didn’t have to say a word.

Emma Colner: And then the third scenario, collaborating with business partners. So let’s say I’m working on a new feature with my business partner. We’ve met and talked about the project several times. We meet each week, seems like we’re all on the same page, but at some point it becomes obvious there’s been some kind of miscommunication, and the project doesn’t end up as we had expected.

Emma Colner: What do all these scenarios have in common? Well, they all demonstrate a gap between what we as experts think we understand and what we actually understand of someone else’s domain. When experiencing cross-functional teams, this difference in expertise can cause friction and lost productivity. I want to advocate for a solution of adopting a beginner’s mindset. So now the neuro scientist is going to come out of me, and I’m going to have a very, very simplified explanation of how learning works in the brain, and how differences in expertise can lead to different outcomes.

Emma Colner: When we learn something for the first time it’s an effortful process that takes a lot of mental resources and attention, so that’s what those red scribbles represent. Over time, as we change from novices to experts, our brains become more efficient as memories consolidate, and unnecessary information is forgotten. The representation of information shifts from the sensory regions to cortical regions of the brain that operate more heuristically and more efficiently. That’s what all the green squiggles are meant to represent.

Emma Colner: So the more knowledge you have on a topic, the more associations you have built up in your mind, and the greater the network of brain areas that are involved while working on a problem. What do I mean about our brains becoming more efficient? I’ll share with you a study called The Development of Expertise in Radiology, and they basically showed that expertise can reduce the complexity of the environment. They did this by showing chest radiographs to novices and expert radiologists and they tracked their eye movements.

Emma Colner: They were told to detect some kind of an anomaly. You can see on the left that … The red represents more time looking at a certain spot, and green is less time. What we see is that novices are just kind of looking all over the place. They don’t know what they’re looking for. They’re spending a lot of time just kind of lost, whereas the experts, it seems that their attention is automatically drawn to the important aspects of the image.

Emma Colner: So that actually, the knowledge that they’ve had over their experience has helped reduce complexity and made the problem easier to deal with. Novices, on the other hand, are using more rudimentary tools. They might take longer, or it might be harder to solve the same problem. Most of the time being an expert works to our advantage, saving us time and energy, but in certain cases, like in the three scenarios I talked about earlier, it can be a handicap.

Emma Colner: Why might that be? Well, the price of expert efficiency is that the scaffolding, or the context surrounding when you first learned something, has been forgotten, probably by the time you’ve become an expert. So just as your brain actively consolidates memories it wants to keep, it forgets most of our daily experience, so over time we only remember the important stuff. Sometimes the only way around a problem is to work through it from the bottom up, starting with basic concepts and building up your understanding, rather than starting from an existing mental model and working down.

Emma Colner: That way we’re forced to think more deliberately, which helps expose weaknesses in our logic, and in other words it helps to just adopt this beginner’s mindset. Circling back to the different scenarios, on the left, that represents my mentor. He was working with a full-fledged Lego set with pieces that all fit together in a sequence that makes sense. And that’s actually like Legos. Whereas I, on the other hand, was working with a bunch of wooden blocks and playing around, trying to stack one idea on top of another, hoping it doesn’t fall down.

Emma Colner: I think what might have happened with my mentor is maybe like he had lost the scaffolding, he had lost the context of when he’d first learned a certain topic that he was trying to explain to me, and so it becomes harder to kind of connect with someone who has such a different skill level.

Emma Colner: And then in scenario two, I was basically describing rubber ducking, which is a debugging method where you basically explain your code to some inanimate object. It doesn’t matter what you explain your code to, but it’s surprisingly helpful in letting you know what it is that you’ve done wrong. The reason it’s so helpful is because you’re forced to approach a problem from a different perspective. You’re building up and filling in the scaffolding that you had lost previously, and that can help us gain some new insights.

Emma Colner: And then in Scenario three, when I describe how I’m working with my business partner, where we didn’t fully connect on our vision, what happened was we both had like a false understanding of the problem and/or the solution. We’d both made some kind of assumption about each other’s work without even thinking about it, because we were both experts in our own domains, and our brains are filling in the details of things that we might not understand fully.

Emma Colner: So, yeah, really, this picture is a joke, but it just demonstrates how easy it is to misinterpret things that might seem really easy. Lastly, I just wanted to return to the skyscraper and reconstruction metaphor for mental models. So while it’s being built up, there’s scaffolding all over the place, allowing workers to place one brick on top of another, but when construction is done, the scaffolding is taken away and all that’s left is a perfect shining tower.

Emma Colner: It can be hard to remember how we arrived at a conclusion once the scaffolding is gone. The next time that you’re collaborating on a project with someone of a different background, remember that you don’t see the path that they took to get to where they are, and it’s often necessary to spend the time to translate, describing one person’s solution in a language the other person understands. Or better yet, coming up with a common language together. Thank you.

Lila Bowker: Thanks Emma. Hi, my name’s Lila, I think I met most of you when trying to sort out the name tag situation up front. I’m just here to make the transitions less awkward, but there’s no guarantee that that’s actually what’s going to happen, so I have notes. Thanks, Emma, for the reminder of how continuous learning and kind of taking a beginner’s mindset is incredibly important as we work with our cross-functional partners.

Lila Bowker: Next up we have Anna Schneider. She’s a manager on our merch algo’s team, and she’s going to talk about how she partners with experts in merchandising to help make our buying better. I’ll give you that. Oh, you have LaCroix. How are you going to switch slides with LaCroix?

Data Science Manager Anna Schneider gives a talk on “Transforming the Way Merchants Find What They Love” at Stitch Fix Girl Geek Dinner.

Anna Schneider: I’m going to put it down, is what I’m going to do. Hi. Yeah, so I’m Anna. As Lila said, I’m a data science manager here. I’m going to talk about a project that I worked on that is very similar to the scenario three that Emma was just talking about, where you’re working with a cross-functional partner, and you think that you’re solving the same problem, and it turns out there’s a whole different kind of problem, and a whole different kind of solution that was needed.

Anna Schneider: So when I say buying better here, I’m not talking about how clients buy better stuff from Stitch Fix, I’m talking about how buyers who work at Stitch Fix buy better stuff to send to clients. So digging in a little bit more, we have a team of people called buyers who are Stitch Fix employees, and their job is to figure out what we should be stocking in the warehouses. That determines the pool of merch that then the stylists can choose from when they’re deciding what to send to a particular client.

Anna Schneider: And upstream from the buyers, the buyers work with vendors to figure out what they should be stocking. So by working with the right vendors and buying the right things from the vendors, the buyers have a huge influence on the end experience that the client’s have, by making sure that we have really good stuff in the warehouses. That’s going to be good no matter who shows up as a client who wants a Fix.

Anna Schneider: If you think about what the buyer’s day would look like, an old school company or at Stitch Fix in the early days, this is what a buyer would do. They would look at a list of things that are on offer from the vendors, and because if you’re lucky to work at a place like Stitch Fix, there’s going to be some performance metrics associated with each one of these items of clothing. And so the buyers would look through a list like this, pick the things near the top of the list, and that’s what we would buy, and that’s what the clients would get sent.

Anna Schneider: This has a number of problems. One of them is that it’s kind of like a standard e-commerce experience for the buyer, so they have to like search through all these lists and hunt and peck, and decide what merch to be carrying. One of the big values of Stitch Fix is that we don’t make our clients go through that experience of doing all the e-commerce shopping themselves, so why are we making our in-house buyers go through that experience?

Anna Schneider: We thought there must be something better. And another more subtle problem with this way of buying, is that by only looking at one single performance metric, you’re only buying for the average client. So let’s dig a little bit into why this problem comes about.

Anna Schneider: Say we have these bunch of clients and we’ve sent them these shirts, and we have some performance metric for each of these. So maybe they all hate the yellow shirt, maybe the gray shirt does really well, and the two green shirts are like, nah, kind of in the middle. If you were a buyer looking at this data, you would think that, oh, the right thing to do is to buy a bunch of the gray shirt, and not buy the others. Seems good. Seems fine.

Anna Schneider: Now, what if we dig into the data and pull out some niche client segment that has different preferences than all the others. The top line shows pretty similar data to before. Those people still like that gray shirt, but the client segment on the bottom likes something else. They like the striped shirt on the end. Now if you’re a buyer looking at this data, the right thing to do is to buy both of these shirts, that way no matter who shows up there’s going to be something that’s really good for them.

Anna Schneider: And this is our mission at Stitch Fix, is to have something that’s going to be good, no matter who shows up. And so it’s really important for us to be enabling the buyers to do the job of buying a really diverse set of merch, that’s going to be really good for a diverse set of clients. So we, on the algorithms team, stepped back and thought about how can we give the buyers this data in order to make better decisions.

Anna Schneider: We thought, well, if giving them one list is bad, what if we give them multiple lists, one for each client segment. So algorithms, and engineering, and merch all collaborated to build a tool that did that. There was multiple lists, so every item of clothing would have how good is it for this client segment, how good is it for this other client segment, and the buyers would go through and choose things from the top of all the lists.

Anna Schneider: This was great. So was it going to be a better experience? We hoped so, but no. It was better at accomplishing the goal of having something that’s good for a lot of different kinds of people, but it made the hunt/peck problem way worse, because they had to dig through a whole separate list for every client segment. They were working harder instead of working smarter to accomplish this goal.

Anna Schneider: And they were like, well, we can do it with a handful of client segments, maybe, but Stitch Fix wants to get to thousands of client segments, millions of client segments, and this was never going to work. Is not a scalable solution. Something really interesting happened as we in Algorithms were talking with the buyers about all of their struggles using this tool. They started seeing some things that should have been obvious, and now that we learned them, it is things that we really care about taking into account.

Anna Schneider: They’re running a real business, and they didn’t give us this feedback in rap battle format, but they almost may as well have. So when they are thinking about what to buy, they have a huge number of other things that they’re keeping in mind, not just this one metric. In addition to having the right diversity across clients, there’s also things like having different price points, having different size and fit preferences.

Anna Schneider: We want to make sure that we really cover all of our bases in those kinds of ways. We’re running a profitable business, as Cathy said. We care about hitting our revenue and margin targets. Buying is something that happens on a seasonal cadence, and so we want to make sure that the stuff we’re buying is going to be good a few months from now. And there’s also other targets around the sort of inventory level management, so are we buying too much and we won’t be able to sell it? Are we buying not enough and there’s going to be missed opportunity?

Anna Schneider: The buyers were thinking about all of these things in their heads while using our tool, and not being able to fully actualize in their role. When we heard all this feedback, we stepped back and thought about, okay, is there a better way that we can be addressing this problem? There’s this performance metric that we want to be maximizing, and there’s all of these constraints that we have on the business. What does that sound like? And we go and like dug around in our algorithms toolbox and said, “Hey, that sounds like constrained optimization.”

Anna Schneider: So we reformulated this as a constrained optimization problem. Our decision variable is the number of units of each item to be buying for each client segment. We choose those units in order to maximize the predicted performance of the whole assortment overall. So we add all that together, as subject to all of those constraints that we talked about on the previous slide. It took a fair bit of work to formulate all of those as equality or inequality constraints, but we were able to get close enough for most of them, through a lot of partnership and talking with merch about like, “Hey, so when you want to meet this margin target what does that really mean?”

Anna Schneider: We were able to get all these formulated. To solve this we’re using a open source Python solver called Pyomo, and it’s been working pretty well for us, even though the documentation is pretty bad. If you’re interested in checking it out yourself, I would recommend googling this blog post by one of our other data scientists who is using Pyomo on a different team within Stitch Fix algorithms. It’s way clearer explanation than like anything else on the Internet.

Anna Schneider: Then in order to make this algorithm available to the buyers, we built a tool that they use to interact with it. So from their perspective, they actually specify values for all of these constraints within the tool, and then press go. And what happens then is that our algorithm, running Pyomo, combines those constraints with a separate algo’s predictions of how well each item is going to perform for each client, and spits back out the recommendations.

Anna Schneider: And so within just a couple of seconds the buyers get the whole assortment recommended returned back to them. This has been a way better experience for everyone involved. So our goal of having something for everyone, this achieves. We’re optimizing over all of the client segments at once. And something that has been really interesting to work on is that it totally changes the contract between the buyers and the algorithms, so the buyers are now responsible for having the right goals for the assortment, entering the right targets, and the algo is responsible for figuring out the best way to achieve those goals.

Anna Schneider: And so it’s almost like the experience of getting a Fix, where as a Stitch Fix client you’ll say like, “Hey, I want a cute dress for a wedding next month,” and the stylist will go off, and the stylist algorithms will go off and figure out the best way to achieve that goal for you. Now, the relationship between the buyers and algorithms is much more like that, instead of the standard old e-commerce experience.

Anna Schneider: Some lessons that I’ve learnt through this that could hopefully be transferable to some other business context, like whatever you’re working on. One is that algorithms are good at tactics, and people are good at strategy. In the old versions of this tool, the people were responsible for the tactics of digging through all the lists, and nothing within the tool was responsible for the strategy. Now we reformulated the tool so that the people are doing the strategy and the algo is doing the tactics, and that’s a much more powerful human-in-the-loop algorithm.

Anna Schneider: One important enabler of allowing people to handle the strategy, is being able to capture that strategy for the tool, and so that’s been the really interesting user experience problem, because sometimes the buyers knew explicitly what their targets were, and sometimes it was all just implicit in their head, and so that’s been a fun partnership experience too, is figuring out a good UX to tease those implicit intentions out of the users, and make them explicit.

Anna Schneider: If we were only working with collect telemetry kind of data, instead of asking like, “Hey, what do you actually want?” it would have taken much longer to figure out what some of these targets were. And last but not least, especially if you’re working in like B2B or enterprise, your users are running a real business, and their jobs are way more complicated than you realize, no matter how much time you spend getting in the weeds with them.

Anna Schneider: So in my experience, at least, it’s really worth getting into the weeds to try to figure out how their processes actually work, and then abstracting it back to something that can achieve their goals even better than they thought and you thought that could be possible. So thanks.

Lila Bowker: Yeah, thanks Anna for explaining how you use data science to make our merch buying process better. I’m going to look at my notes again, sorry. Next up we have a dynamic duo, half of which is already up here. Come on over. All right, so we have product designer, Bing Tang. Oh-oh, there goes my notes. And data scientist, Erin Boyle, and they’re going to explain how we use data to understand client style, and then how we use that to inform stylists to send better Fixes. Take it away, guys.

Data Scientist Erin Boyle and Senior UX Designer Bingrui Tang give a talk on “Partnering on Style” at Stitch Fix Girl Geek Dinner.

Erin Boyle: Thanks, Lila.

Bingrui Tang: Okay, hello everyone. My name is Bing, and I work on the UX design team, [inaudible]. And this is Erin, she works on the data science team and working on AI instruments, and today we’ll be … being the dynamic duo, and talk about communicating style together.

Bingrui Tang: I think Cathy already talk about this a little bit, but at Stitch Fix we really have this human-in-the-loop process. We have algorithm empowering the stylist’s work, where a stylist will actually make the call. And if we actually put our foot into a stylist’s shoes, it is a very challenging work. Imagine you have a client who is there in a 50s Fix, and their profile might haven’t been updated for a while, and their style preference has been changing over time. It is really difficult for a stylist to dig through all the things, and do we really want to empower them to use their creativity to really send a delightful Fix to our clients?

Bingrui Tang: So on the backend side, a lot of our work is really to design for the styling platform, so, as you can see here, on a platform we would combine the client data which has some basic information. Their style preference and their Fix history, as well as how the algorithm is recommending the items of a variety of categories, and decides by going through all the information about a client, they would actually be able to pick the items that they think would fit the client well, which might not always 100% fit with how the algorithm sorted out.

Bingrui Tang: So for us, a big part of our work is to make sure the way we present the client data is really helpful for the stylists, so they can really do their job well. Previously, the way we represent a client’s style is more aesthetic, so for those who have been using Stitch Fix before, when clients sign up they would rate outfits of different styles, and we will translate this summarized data into some different formats, to the platform, so that the stylists will see and be able to understand what the client wants.

Erin Boyle: Okay. Cool, thanks. The data science team that I’m on works on contributing to a broad set of problems, kind of in this category, that rely on having some more nuanced definition of style. So we actually built a new platform to try and support this. Can I ask, has anyone in this room played style Shuffle? Got a few hands. Okay. Style Shuffle is a kind of game-like experience that we released a little over a year ago, where clients can rate items in our inventory, thumbs up or thumbs down.

Erin Boyle: It’s been really fun and engaging for them, people really like giving us data like this. We have more than a billion ratings now. You can imagine that, like Bing said, one way that you can represent someone’s style is with these kind of like limited number of static questions that we used to have in our style profile. But you can imagine that there’s a few limitations to that, like one, it’s just not very much data, so it’s not going to have a lot of nuance, and then like Bing said, it will also get stale over time.

Erin Boyle: So since people can continually play this game, we can keep collecting more information and get kind of richer, and richer, and up-to-date information about our clients. It’ll come as a surprise to no data scientist in this room, that one way you can deal with data of this format is with an algorithm called matrix factorization. If you want to come up with descriptions of a client’s, what we’ll call, latent style preferences, one thing you can do is you can treat the ratings coming out of this system as kind of sparse observations in this user item matrix, where every row is a user, and every column is an item.

Erin Boyle: You can decompose this matrix into a lower rank co-representation that is composed of the product of this lower dimensional user embedding matrix, and lower dimensional item embedding matrix. When you’ve done this, what you have learnt, you’re learning the coefficients in these two matrices on the right. What you’ve learned are what we call latent factors, that describe client preference. So the kind of columns in this user matrix and the rows in this item matrix are going to describe … they’re going to be kind of like hidden underlying variables that describe a big sources of variance in the preferences that we see from our clients.

Erin Boyle: If you want to get a prediction back out for any of these user item pairs that we haven’t actually observed. If you want to get back out a prediction of how well a user might like some particular item, all you need to do in this framing is simply take a dot product of this user and item vector to recover a kind of score for that pair.

Erin Boyle: In reality, we might actually expand on this, and use a more nuanced algorithm, but I think this is a good kind of framing to understand it. What can we do? So one thing you can do once you’ve trained an algorithm like this, is you can simply pull out the top recommendations for any client. Here I’ve sampled five real clients form our clientele, and I’ve also sampled some of the top items, given this prediction task for these clients, and you can see that you really uncover using this algorithm, like a range of different aesthetics. You can see that these people are all of different style.

Erin Boyle: That’s one thing that we can provide to technical and business partners like Bing, to paint a picture of our clients. But we are interested in also providing something else, so just like from the style profile, you have this set of user features that comes out. We’re also interested in whether the user representation, like just this user vector on the left here, was kind of in and of itself useful and interesting, and whether we could use it as a feature that we could communicate through language as well.

Erin Boyle: One way you can do that is to really think about this kind of style space that you’ve created when you run this algorithm. So let’s say we ran this algorithm with just three latent factors like we’ve done here, just three columns in the user matrix, that means that once we’ve learnt coefficients for this user, they have a location in this kind of XYZ space. They have an address. We’re curious, does that address itself kind of convey information?

Erin Boyle: One way you can dig into that and try and come up with a language to describe this user representation from this algorithm, is you can actually look at these axes, and you can see what they represent. We’ve found these latent factors that predict client preference, but what do they mean? Like what’s going on? One thing you can do, an item that is good for this user … Since our prediction task is done by taking a dot product, an item that is good for this user is going to be an item that is near this user in this style space, and an item that is bad for them is going to be far away.

Erin Boyle: One thing you can do, is you can look at how the items change across some particular axis, to come up with what this axis is encoding stylistically. When we do this for this one X axis, you can see that this is sample of items on the left, and they have this kind of boho style to them. And this is a sample of items on the right, and maybe you would call them more preppy.

Erin Boyle: And you can then go through and do this for the major, what we would call, principle components of this style space in order to really explain what this user’s representation means. There’s a couple little technical details if you want to do this in real life. I actually have a blog post on it, if you want to dig into that.

Erin Boyle: But basically what this gets us to, is that our team can kind of partner with people like Bing, to provide these intermediate data products, that they can plug into whatever their domain is within the company. One thing might be actual recommendations. One might be the client’s representation. Maybe on merch they might care about the style’s representation instead. What we try to do is just provide people with whatever products are going to be useful for their application.

Bingrui Tang: Thank you. Thank you, Erin. I know it’s very magical, isn’t it? I remember when I first got on the project, I was like, “Ooh, look at that.” So, yeah, that’s come back to what I was talking about, designing the styling platform. Once I wrapped my head around this project and going through many meetings with our cross-functional partners, there’s two big questions sitting in front of us.

Bingrui Tang: One is, how to make sense of latent style? Because, as Erin just said, it is a very mathematical model. But we want to make sure it is something that somebody who doesn’t really know statistics, or doesn’t really know much about math, could still understand it in some way. And more importantly, we want to make sure that it could stylists actually make decisions, and with confidence.

Bingrui Tang: So for these two questions we actually conducted two different studies and I will just quickly go through them, and share with you guys. The first one, as Erin just said, and she actually showed these two clusters of clothes too, I think most people would see, “Oh, these two are different styles.” But it’s very hard to say why, or how, or what, or exactly, so this is also how we started the study, because, as a human, when we see a bunch of things sitting together with similarities, we just have this natural tendency to give it a name, a label, a theme, or something like that, so that was how we started.

Bingrui Tang: Let’s use this, two extreme maxis as example. The first moment we saw it, we said, “Oh, the left-hand side looks very classic. The right-hand side look trendy.” Yeah, something like that. But because we thought this is coming from the mathematical model, we should really think about it very objectively. So we say, what is the most visible objective factor? Let’s say, the left-hand side is very less skin exposure, it’s much more covered, and the right-hand side looks more skin exposure, what about that?

Bingrui Tang: And then we show it to the stylists and also other people on the team, and then there has been a lot of voice raised, because everybody’s like, “Yeah, there is skin exposure, but the print seems different, there is seasonal implications. The color scheme is different. The fit is different. There is different levels of embellishment. Which one is the most important? How do we kind of …” Then there’s a lot of debate around that.

Bingrui Tang: At that point, the team and everybody, we actually started stepping back a little bit and think about what is style in reality? This is one quote from the movie The Devil Wears Prada–I still remember when that quote come out in a movie–but I think essentially it is talking about what style was really manifested over time, from the cultural and the historical influence. For us, it is really important to recognize and embrace that instead of trying to invent some new way of talking about style overnight.

Bingrui Tang: At the end we actually come back to our original idea, and actually showed it to stylists, and see how they reacted to it. Do they recognize these words as representative of these clusters? And seems like the old way, the classic and trendy, actually worked the best.

Bingrui Tang: For the second study, I think Erin also showed this image before, now that we know we can get a sample set of items that seems to be into the client’s preference, and we can also get a relevant location of the client preference in this spacial model, how can we display the client’s style representation in the styling platform so that the stylist can actually understand it within a short amount of time?

Bingrui Tang: The goal for us is to find the right way to display each client’s style, to help stylists actually style a Fix. So, again, oops, let’s use this image as an example. Imagine this is client 123, that’s her style preference sample. So we tried a few options again. The first one, we said images only, here’s what she likes, and you can interpret it as much as you want. And we were not feeling quite 100% confident with it.

Bingrui Tang: And then the second option is the more mathematical one, we say, “Her style is 53% trendy, and 28% boho.” And we definitely know it is a little bit too extreme, so we also pulled back a little bit, and look at this, the happy medium. So we would say, “Her style is very trendy, and a little bit boho.” And now we have three options later, we actually ran a quick study, and we are surprised.

Bingrui Tang: We compared a bunch of different factors, from how accurate the stylists are able to find what the client likes and figure out what the client dislike, and also how fast they are.

Lila Bowker: Try that.

Bingrui Tang: Okay. Oh, thank you. And also how the stylists feel about using this feature as an experience, and seems like the image only one actually won all of them. And from the result, seems like, at least during the experiment period, we learned that displaying the images only has the best overall outcome.

Bingrui Tang: So when we look back at the long journey, we realized at the beginning we were trying to display as much information as possible and tried to put massive amount of data in front of stylists, and then we pulled back a little bit, and then we realized we can actually let the information speak for itself, instead of we try to add more things on top of it. Which reminds me of a quote from the very famous designer, Dieter Rams. Her work inspired a lot of Apple products, and he once said, “The good design is as little design as possible.”

Bingrui Tang: I think that really speaks … the philosophy’s really representing how we work here.

Erin Boyle: Awesome. So I guess I’ll add to that, is that … Ooh, hold that thought.

Lila Bowker: Keep talking. Keep talking. Just pretend there’s the lady behind the curtain over here. Oh, no.

Erin Boyle: This collaboration was really interesting for me, too, because A, it was really interesting to learn that it’s hard to summarize aesthetics with language, like I think of language as being very rich, also, but in this case it wasn’t quite up to the task. Our brains are really good at processing images, so that was a good thing for us to learn kind of broadly, even outside of your use case.

Erin Boyle: And then also, I still got the benefit of having language that experts had applied to this space for cases where you really need language, like you can’t always show a collection of images for every kind of use case, and so we do still have language that we’ve gotten from a bunch of the work that Bing’s team did. So, this is an example of one of many technical partnerships that we do here, and thank you for listening.

Bingrui Tang: Thank you all. So I believe most of the teams that are representing here are hiring. Lila will speak more about that, but design team is hiring, so we can talk later. Thank you.

Lila Bowker: Thanks, Bing and Erin. How they takes styles, shuffle data, and turn it into better Fixes for clients is one of the reasons that I joined the company. I love it. Next up I wanted to introduce Erin, she’s a principal engineer on our Fix request team, and she’s going to talk about the importance of giving feedback, and some strategies for how you can give feedback more regularly. Go for it.

Erin Dees: Hi, friends. Is it on?

Lila Bowker: Try this one.

Principal Software Engineer Erin Dees gives a talk on “Dossiers of Awesome: One Way to Help Folks Get the Recognition They Deserve” at Stitch Fix Girl Geek Dinner.

Erin Dees: Hi friends, how are you all? One more talk on the theme of partnership before we all get some networking time. The focus here now is going to kind of narrow in on the personal partnership, because as strong women we are told that we need to lift one another up. But how? Now part of the answer lies in helping our peers get the recognition and the visibility that they’ve earned.

Erin Dees: It’s about giving meaningful feedback that lifts them up, and also helps you. So this talk will be … The title, Dossiers of Awesome, is just a way to frame these habits and practices. It is not a complete solution to how to lift one another up. It is not a universal recipe. It’s an idea. What I’m really hoping with sharing it, is I get to hear your ideas afterwards. So let’s talk during networking time.

Erin Dees: My name is Erin. I joined Stitch Fix about two months ago as a principal engineer. These are my goats, it’s the day I brought them home in my Kia.

Lila Bowker: I’m so sorry, we’re having … Try this one. I don’t know, I’ve mixed up which one was the good one.

Erin Dees: Can everybody hear me now? Is this better?

Lila Bowker: Is that better?

Erin Dees: Okay.

Erin Dees: Thank you, friend, sorry about that. All right. You didn’t miss anything. Something, something goats. All right. Okay. I really enjoy working on a big thorny systems engineering problems, the kind where you have to reach out across teams, learn from industry, learn from one another, and then package that learning up and bring it back to your team. That’s my jam, like sharing knowledge like that. That’s why I write programming books. That’s why I coach athletes how to race walk faster. It’s not why I have goats, but they are very good listeners.

Erin Dees: This talk came out of a conversation that I had with a former colleague, Liz Abinante, a few years ago. We were talking about this series of stories we’d read, prominent women leaving their posts in the tech industry because of being passed over for opportunities, and being harassed. I said to Liz, “How do I get better at observing, at noticing? I would hate to think that something like this happened to somebody on my team, and I didn’t even see it. How do I catch this happening?”

Erin Dees: And Liz said, “People aren’t going to harass in front of an audience. Right? They’re not going to harass in front of witnesses. So, that’s not the way. If you want to help your teammates, that’s not really a great way to do it.” And I said, “Well, how do I help my teammates? Half my teammates are women, how do I … I like them, I like working with them, how do I hang onto them?”

Erin Dees: And Liz said, “Help them get the visibility and the recognition that they have earned.” The conversation branched out from there, we brainstormed a lot. But I want to pause here for a second, and say that even though this idea came out of a difficult situation and a tough conversation, it’s going to apply in a lot of different scenarios, and I also am aware that a lot of the people in this room are already doing a lot of emotional labor for their teams. The last thing I want to do is give you all more homework.

Erin Dees: I really want to talk about this in terms that will help you in your careers as well. How many of us are in a job where we are expected to give feedback on our peers regularly? Right. A lot of us, right. How many people kind of dread that time of year when you’ve got to go write a bunch of peer performance reports, right? It’s exhausting. It takes forever to write, and by the time you’ve done your fourth or fifth one, it’s hard to come up with something that is unique that could only apply to that engineer.

Erin Dees: Like, “Well, you’ve built some great products for us, and keep learning more advanced Ruby skills, I guess.” Like I mean, right, that could apply to everybody. So how do we get feedback that helps that specific person get better in their career? And that’s what we’re going to talk about. So in order to achieve this goal, whatever process we adopt should be lightweight, because we’re all busy and we’re just not going to do it if it’s too much of a burden on our time.

Erin Dees: It should be something that we keep up with in little increments throughout the year, instead of having a big deadline dropped on us. And again, it should be actionable, it should give our peers information that they can use to grow their career. One idea had been sitting right in front of me this whole time, which is an engineering journal. Now, I had started keeping this a few months prior, because we had formed a brand new team, and there was so much learning for all of us, that we really had to capture what we’d learned and what we’d done in some way, and my solution was to keep a journal.

Erin Dees: I tried to do it every day, ended up doing about 50% of days, and here’s something like what that looked like. 4:30 p.m. every day right before the end of the workday, a blank window pops up on my desktop, it’s time to write in your journal. And I spent just a few seconds, literally just a few seconds typing in a couple of bullet points. I implemented this feature. I fixed some tests. It’s okay to get snarky, it’s your own journal. Nobody’s going to read it. And the twist here then, was to realize that there’s no reason I couldn’t put anybody else’s accomplishments in here.

Erin Dees: Sometimes my journals talked about stuff I’d done with my teammates, but it was time to get systematic about it, and use hashtags and stuff. And so that’s what I started doing. So in addition to what I’d worked on, I might add a couple of bullet points that a teammate worked on, and tagged them with a hashtag, which comes in handy later. So then if you’re in a culture that does sort of quarterly feedback cycles, when it comes time to do this, I can click on that person’s tag in my journaling software.

Erin Dees: This is totally real data you all. I did not just type a bunch of this stuff at 1:00 a.m. in my hotel room last night. So then you can either copy and paste, or kind of paraphrase, but you start with a blank document, and you can paste all this data in here. And you start to notice as you do this that these items kind of fall into patterns. These few items seem like they’re about incident response. These couple of items here seem like they’re about tech leadership, and so you can group them. You can start moving them around and you can add some headings.

Erin Dees: Now, then what you do with this information depends a lot about your feedback culture. If you’re in a place where you’re expected to write your own review, for starters, a self-review, you can give your peers the ammo, the raw material that they can use to write their self-review. So here’s what that might look like, you can compose an email, and if your manager is someone who’s supportive, write it to them and Cc your friend or your peer. Well, I hope they might be your friend too. And this now tells them a story. If they’ve been waiting for a great opportunity to write a promotion pitch for this engineer, you’ve just given them all this ammo.

Erin Dees: But the audience is also your peer, because if they’re going to be writing their self-review, it’s going to be a bunch of stuff that they may not remember in the moment that they accomplished that quarter. That said, though, do please let the manager know what they’ve done as well, because there’s a lot of cultural pressure on us not to brag, and we should fix that too. But if this is all stuff that happened, this isn’t bragging. It’s data.

Erin Dees: It’s a good idea to share it with … again, if you have the supportive manager, and with your peer. So how to make this feedback actionable, so that somebody can act on it and grow their career? One way to do this is to work these data points into a story, so it’s not just data, it’s a narrative. What this looks like, for example, if you start noticing this person developing or showing an aptitude and interest in tech leadership, is to call that out, and say, “Hey, maybe it’s time to start handing this engineer larger projects and have them run bigger initiatives. They seem to have a knack for it.”

Erin Dees: So I want to pause, just one second, and say that we’re at a women’s conference, and we’re talking through this originally through the lens of a conversation about women in tech, but there are lots of teammates that we have that are dealing with other marginalized identities, some multiple marginalized identities. So I want us to keep in mind all of our teammates who might be marginalized along one or more axes when we think about lifting one another up.

Erin Dees: That is one way that we can have an impact on our careers and our peer’s careers, is lifting them up. But again, this helps us do our jobs better. If we’re expected to give feedback, and we can do something that gives better and high quality feedback more quickly with less overhead, that helps us too. That makes us recognized for giving good feedback that really helps people, and that’s the impact I’m hoping that you all will have, no matter how you all choose to do it.

Erin Dees: So I’m really grateful to Girl Geek and Stitch Fix for putting together this event. Lila, this has been amazing. I love it. I have loved every talk I’ve watched. The other presenters, you all are amazing. I’m really grateful to Liz Abinante for this original conversation, and Lila and Miriam for helping me, appropriately enough, with meaningful actionable feedback about this very presentation.

Erin Dees: As we prepare to head into networking time, I want to come back to that cultural expectation that we don’t brag, and I want to chip away at that just a little bit tonight. I’d like to invite you all, as you’re introducing yourself to other people, to say something that you’re awesome at, work related, non work related, doesn’t matter. Lead off with something you’re skilled at. And if that’s one notch too extroverted for the comfort level tonight, maybe ask the person what they’re awesome at. Just know that they might turn around and ask you, be prepared for that, it’s okay.

Erin Dees: As we were brainstorming for how to lead into networking time, as a group of presenters, Lila brought up these two articles about the importance of owning your awesome, of owning what you’ve built. I really want to embody that spirit tonight, if at all we possibly can. It’s been hugely inspiring to be in this room full of awesome badass women in tech. I’m really grateful to be up here. I hope you all have a great rest of the event, and cheers.

Erin Boyle: Thanks, Erin. That was amazing, thanks, Erin.

Audience Member: I have a question about the buying process, like particularly more like the supply chain issues you might face. How do you, if you end up with an extra load of clothes that don’t fit anybody, how do you steer away from trying to push product on people just to sell it, and what do you do with your unsold inventory?

Anna Schneider: Yeah. There are several algorithms that help with that, unsurprisingly. So, yeah, we have clearance algorithms that figure out, hey, this thing isn’t performing very well, and then sometimes we will get rid of it through donating it, so that’s one common way that low performing stuff will leave our inventory. Although when we’re doing that, we do want to make sure that we’re not getting rid of stuff that’s like bad for a lot of people, but like really good for someone. We do want to keep that niche stuff around.

Anna Schneider: So with the client’s expectation, like I was talking about, like that’s like something that we’re always trying to figure out how to get better at.

Cathy Polinsky: But I think one of the things that I really appreciate about Stitch Fix is that we separate out the merchandising team who buys the product from the stylist team that sends the product to the right people. So we have intentionally created this firewall between the two, so the stylists are never incented to send bad product out to their clients.

Cathy Polinsky: They don’t know, “Hey, we’ve got a lot of those lime-green shoes out there that aren’t selling, can you just send it out to people.” Instead, we really try to make sure that the stylists are incented to really keep their clients happy, and if we buy bad product, we eat the cost ourselves, and learn from it for the next time, to make sure that we don’t make the same mistake over and over again.

Cathy Polinsky: It is this interesting system where sometimes actually getting rid of bad product helps everybody else up, just making sure that that bad product is not inadvertently getting into Fixes. And so we’ve seen these times where we have changed the dynamics in the model, of how often we’ve gotten rid of bad product, and we’ve now learned that it’s not a good idea to hold onto it, but it’s much better for us to get fresh stuff in. Was a good question, though.

Lila Bowker: Nice. All right. I’m getting my cardio in, you guys, hang on. There you go.

Audience Member: Hi, I’ve a question with respect to recommendations. How do you deal with surprise, because you can learn someone’s style, but often when someone really, really likes something, it might be because it’s a little bit outside their comfort zone, and it’s one item. Do you like work that in by humans or algorithms?

Erin Boyle: Yeah. That’s a great question. And I should say that the exact answer to that might change, depending on what context you’re talking about. So, probably you’re asking about in someone’s Fix, and in that case a lot of that would be done by the human stylist. We do have algorithms that try to think about assortment, but we mostly rely on stylists, or … Yeah? Yeah, stylists would definitely be injecting a lot of that.

Erin Boyle: The other thing I’ll say is another place where you might use recommendations, I mean we have recommendations everywhere, but another place you might use them is like in the stylish level itself. Of course there are recommendations that are fed back to the client, and certainly there is some like assortment logic and experiments and stuff that have gone into like injecting surprise into that experience too.

Cathy Polinsky: But we try to get employees to style, as well as the stylist, and so I try to style a couple Fixes a month, and occasionally I’ll get someone that says, “Stop sending me skinny jeans,” and the recommendations only knows that this client buys skinny jeans, and so the stylist has to think of like, “Well, maybe I’ll send her a boyfriend jean, or a boot cut jean.”

Cathy Polinsky: Our algorithms have no idea what they’re going to want next, based on their previous purchases, but we’ll have to use this … we like to call it the blend of humans and machines. The art and science of what we do is sometimes there is this kind of stylistic creativity that goes into generating a Fix. It’s kind of fun.

Erin Boyle: Well said.

Bingrui Tang: I was also going to add on that, because sometimes there will be clients say, “Oh, I really don’t wear dresses,” and then all of a sudden they’ll say, “Oh, I’m going to a wedding, then I need a dress.” So it’s very important for us, when we design the system, we really keep in mind this kind of flexibility, and people’s preference will change, either occasionally or over time, and we really want to recognize that. I think that’s a really good question, and that’s definitely the fun part of the work, is trying to juggle them both. Yeah.

Lila Bowker: All right, let’s start here.

Audience Member: The question is about the buying process in the old traditional way. One of the reason they’re using that method is because probably the cost optimization, the more you buy, the cheaper it is. But buying optimized by diversity using algorithm, how you deal with the cost optimization to the supplier?

Stitch Fix girl geeks: Cathy Polinsky, Erin Boyle, Bingrui Tang, Anna Schneider and Emma Colner answering audience questions at Stitch Fix Girl Geek Dinner.

Anna Schneider: Yeah, so in the constraints that the buyers will put into the problem, one of them is exactly around that. We know how much we’re going to buy it at wholesale, and we know what our margins will be because of that, and often the algorithm will say like, “Hey, this one has really good margin, buy even deeper into it.” So if that’s something that we want to be … Yeah, yeah, it’s something that’s just rolled into all of our other data.

Anna Schneider: That’s something that we … Margin isn’t the only thing we care about by any means, there’s all these other metrics about making the experience really right for the clients, and so that’s what we are … currently it’s formulated, where we’re trying to give the best experience to the clients as possible, constrained by having a profitable business. Yeah. So the client is really first.

Audience Member: Sure. One of the things I’ve always struggled with with these try before you buy services, is that the things I like aesthetically don’t always match the things that look good on me. I’m curious what you guys think about that, and how you are imagining how the Stitch Fix product addresses that challenge?

Erin Boyle: Yeah. I mean that’s a tough question. I think there is kind of a fine line between aesthetic and fit. I mean even in this latent style data, which we think of as being largely a style thing, like people are rating whether they like an item and it’s a very visual thing, they’re not necessarily seeing the size of that item that they would actually buy, or the can’t tell the inseam or whatever.

Erin Boyle: And yet we do see fit preferences coming out of that data, like we can figure out some fit preferences too. Yeah, I mean I think we certainly try to collect information on both style and fit in the style profile, and through other means, and then certainly the client has a conversation ongoing with the stylist, where they give the stylist feedback on the details of what does and doesn’t work for them.

Erin Boyle: And then those kind of like subtler pieces of feedback that really take a human to interpret can be acted on by that stylist.

Cathy Polinsky: Anecdotally, I see this. I have some clients that have an Instagram feed, and I’m like, “Oh, they liked this item, we have it in inventory, I’m going to send it to them, even though the match score is really low for the product.” And I send it to them and they hate it, but I’m like, “You said you wanted it.” But there is this notion of what they like stylistically, versus what is good on them, and so I’m really impressed with how these match scores that we have factor that into the recommendations.

Cathy Polinsky: And it’s not like, “I don’t want that navy blazer, I want a navy blazer that looks good on me.” And so how can we take the style things that people are sending us, and pair that with other factors, like fit, to find the exact right thing for you. It’s a hard problem, and that’s why it’s so much of a pain for people to try to shop online and scroll through lists and lists of jeans, to figure out what’s going to fit them.

Cathy Polinsky: So I think that this is a different model that we can use, that can factor in a lot of those different attributes.

Audience Member: I found it really refreshing to see multiple senior female engineers on this panel, and I was wondering what your approach was to sourcing and retaining female engineers. Sort of like what percentages of your teams are female, with regard to engineering specifically? And did that change as Stitch Fix got larger?

Cathy Polinsky: We are really fortunate for the gender representation that we have at Stitch Fix. It helps that we have a female CEO and founder, Katrina Lake, who has started this company. Half of our leadership team is women, more than half of our board is women, and a huge representation of our stylists are women, so if you look at our employee count, which is predominantly stylists, it’s over 80% women who work at Stitch Fix.

Cathy Polinsky: And then our technology organization is really strongly represented. We’re in the high 30s for representation of women in our tech organization. It’s still not 50/50, and still opportunity for us to grow, but compared to every other tech company that I’ve worked at, we are really leading the pack at having a very strong gender diversity in our teams.

Cathy Polinsky: We hadn’t done it through top down quotas or mandates, but really it was generated by teams and managers who cared about this. Of understanding that diverse teams build better products, that we had primarily women as our clients, and to really understand them and understand the products that we’re building, having those diverse teams helps us to make sure that the things that we’re building are really strong and supporting that client base.

Cathy Polinsky: What was great for me to see, as we were scaling out our organization, it’s hard when you’re hiring fast, to make sure that you’re thinking about diversity and all of the different criteria, but we only got better as we scaled. It really came down to the managers who didn’t have that diversity on their teams, were asking the other managers who were doing well, like, “How did you do that?” And, “How can I do that?”

Cathy Polinsky: It came through inclusive language in our job postings, or thinking about sourcing in a different way. We talked about experiences that we had to make sure that we had diverse panels, and then we also look for really product centric engineers at Stitch Fix, and I think that that helps us generate a more diverse pipeline of what an engineer looks like throughout our organization.

Cathy Polinsky: We’re not done. We still really want to hire more architect level senior individual contributors here at Stitch Fix, that’s an area that we don’t have as much diversity. And then we’re also looking to increase our racial diversity, and think that we are really leading the way on gender diversity, but have some more way to go in other aspects of diversity, like racial diversity.

Cathy Polinsky: But I love that we’re a group that cares about this, and we talk about it, and we celebrate where we are doing well, and also understanding that we’re not done.

Erin Dees: May I jump in for a second?

Cathy Polinsky: Yeah.

Erin Dees: I joined pretty recently, and it was one of the things that drew me to Stitch Fix, was seeing that there were so many women in engineering specifically, and then as you look up, senior leadership, in terms of like management, there’s a lot of women in senior management as well. That was something that really shines through, so one reason I think that Stitch Fix has so many women in engineering, is because we’re drawn here. We see like, this is a good place for me. None of us wants to be the only woman on the team, so it’s been awesome.

Cathy Polinsky: Great. Thank you. Glad to have you.

Lila Bowker: Another question over here.

Audience Member: Hi. I have a question on I guess the outlook on how accurate you think your algorithms are becoming over time? Are they becoming more predictive? Do you think that we’ll get to a point where the algorithm can handle both the strategy and the tactics? And if we are at that point, do you think that Stitch Fix would ever move to a model where maybe there is less reliance on human stylists? If yes, why? If not, why?

Erin Boyle: I’ll let Anna speak to the strategy tactics, because that’s definitely your framing. I will say they’re definitely improving all the time. Obviously we can’t give you any quantification of that, but I’ve actually been here almost four years, and it’s shocking how much better we are. You can see it in so many ways. So, yes, they’re improving all the time. I still expect them to improve more, but I don’t expect it to really change anything about the role that people are playing.

Erin Boyle: I think that the role our stylists play is always going to be critical. It’s a critical part of our business model, they play a critical role, and similarly with our merchandising partners, and so many other people, so yeah, I’ll let Anna speak to the strategy and tactics. But that would be my reaction to that.

Anna Schneider: Yeah, one analogy that we’ve started using for the strategy versus tactics, is are we building a self-driving car, or are we building something like Google Maps? I’m mean for a self-driving car that’s just like all about the tactics. It’s like … can be completely automated. There’s no human in the loop there, whereas something like Google Maps, it doesn’t even make sense to think about what that would mean if it was fully automated, because who’s telling it where to go? You need someone giving the instruction.

Anna Schneider: And so there’s always going to be that company leadership and leadership at levels all throughout the company saying, “Where are we going?” And that’s always going to be a human decision, and so we think of it almost more like a scenario exploration engine, where you can say, “Hey, what if we went in this direction? What if we went in this direction?” And then the humans have the opportunity to choose what of all possible futures we want to be going after, and that’s always going to be a human touch.

Cathy Polinsky: So any chess players in the room? Yeah. Was it Kasparov who was the chess player who lost against Deep Blue? So we look at the sense of … At that time it was like this is the end of chess, and the age of the machine, and machines are going to always be better than humans, and what was it? So that was when the first chess game played against a human and won.

Cathy Polinsky: But what’s interesting is after that point, there was a new emerging game that was this freestyle chess. Have you guys heard of freestyle chess? So the freestyle chess, is this idea that it doesn’t matter whether you’re human, or you’re a computer, or a blend of the two, but anybody can play against anyone.

Cathy Polinsky: So they have this competition every year and there’s a freestyle likeness, and the first year that they did this a novice group of chess players won using computers, and they did it in a very novel way, of using a suite of machines to solve the problem. It was this really interesting thing, it was the blend of humans and machines. Humans that had an interesting approach for how to solve the problem, but they were backed by computers.

Cathy Polinsky: And so we use this likeness of what Stitch Fix does. It’s not computers alone, it’s not stylists alone, but it’s this blend of humans and machines that work together in a novel way, to solve a problem in a unique situation. And so we feel really strongly about this model and how it’s helped us, and sometimes we’ll lean a little bit more on the machine side, sometimes we’ll lean a little bit more on the human side, but regardless of where that line is, we feel like the power of the two together is really a magical thing.

Lila Bowker: Awesome. We do want to leave enough time for folks to chat afterwards and brag about themselves and hear what makes everyone else awesome, so maybe one more question, is that all right with you guys? All right, here you go.

Audience Member: Hi, sorry, one of the last questions. A lot of fashion is traditionally geared for women, so I was wondering if you have seen patterns that are maybe encouraging males to be a little more adventurous in their stylistic choices? Just out of curiosity, you know, I think … I mean, I don’t know, but personally I have brothers, that I would like them to be more adventurous, and it’s hard to get them into the space.

Audience Member: I don’t know if you have encountered patterns or had strategies, or even have some vision for even working with fashion industry to sort of expand, that fashion’s not just for women, but also for people who traditionally are not associated with fashion?

Cathy Polinsky: We started out as a women’s only business, and for the first five and a half years we only had women clients, and then about two, two and a half years ago, we launched our men’s business. It’s been really great to see that business growing and thriving. I’d say, I don’t know if it’s specific to gender or not, but we see some clients that start out and they have a specific sense of what they’re looking for, and as they use a stylist, over time, they get to be more adventuresome. And they might try new things and get some of these serendipitous things in their Fixes that they never would have picked off of a rack before, and try it on.

Cathy Polinsky: You know, we see this with our women clients, but I think we see it a little bit more so with our male clients. And I think that that is just kind of a fun thing about our service, is that something that you might never try on, something that you may see in a Fix, we don’t even show you what’s coming, because we don’t want it to taint your view of the Fix before it arrives at your doorstep. And sometimes there’s something that you never would have like wanted in your Fix, that you try on, and I’ve had this happen to me as well, that I’m like, “Oh, this looks amazing.”

Cathy Polinsky: And so I think that that aspect of discovery is a really amazing part of our service, that it works for those folks that may be stuck in their style, whether you’re guys, stuck in a rut, or even a gal, yourself. And so it’s kind of a fun part of our surprise and delayed-esque model. I hope I answered that.

Lila Bowker: Bing had a client recently that was awesome. Bing, you should tell that story.

Bingrui Tang: Oh, yeah.

Lila Bowker: Remember your client?

Bingrui Tang: Yeah. So I had a client who is probably in his 40s and 50s, and I got his first Fix, and he said, “Oh, yeah, so my style is a little bit conservative, really boring, but I want to make my wife happy, so I really want to stretch it out a little bit.” This is his first Fix, and he such high expectation, so I’m really nervous, and I share it with the team, I say, “Yeah, what do you guys recommend?”

Bingrui Tang: And somebody actually recommended a conversational piece, which is essentially pink flamingo prints. Yeah, and zebra prints, and things like that. So I actually sent a bunch of them to him for the first Fix. He returned all of them. I think I definitely went too far, but he said, “Thank you for all the pieces you sent, I think it’s a little too stretched for me, but I really get idea, and I really want to try it again.”

Bingrui Tang: And he actually immediately scheduled another Fix, and so assigned me as his stylist. So for the second Fix, because now I know where he would be more comfortable with, I actually pulled back a little it, and I think he kept all of the five pieces. Yeah, so I think, back to your point, I really think, yes, I’ve sent many, many pink shirts to male clients, and a lot of them end up keeping them, so I think, yes, the discovery part is really the fun part.

Lila Bowker: Awesome. Well I think we want to open it up so everybody has the time to chat and brag about themselves, and hear what makes everyone else awesome. The quick plug of, of course, we are hiring. If you work at Stitch Fix, can you raise your hand? Everybody who works at Stitch Fix, stand up and raise your hand. Yeah, so if you want to chat more about the roles we have open, aim for one of those human beings that just raised their hands.

Lila Bowker: But I’m excited to get to know more of you better. Enjoy. Yeah, and thanks for coming.

---

Our mission-aligned Girl Geek X partners are hiring!

• See open jobs at Stitch Fix and check out open jobs at our trusted partner companies.
• Does your company want to sponsor a Girl Geek Dinner in 2021? Talk to us!

Speakers:
Heidi Williams / CTO & Co-founder / tEQuitable
Angie Chang / CEO & Founder / Girl Geek X

Transcript:

Angie Chang: Can you hear us?

Heidi Williams: I can, yep.

Angie Chang: Wonderful. Well, welcome back to Girl Geek X Elevate. I’m Angie Chang, founder of Girl Geek X, and yes, this is being recorded and the videos will be available later online in about a week. So give us some time. Please tweet and share. The hashtag is #ggxelevate. We’ve been seeing a lot of great comments and pictures of viewing parties, so please tweet them at us and we’ll retweet and share. If you have a question for Heidi, our next speaker, please be sure to put it in the Q&A below and she will answer them after the session.

Angie Chang: So now we are excited to have Heidi Williams, the CTO and co-founder of tEQuitable share with us about intersectionality.

Heidi Williams: Thanks, Angie. I’m going to get my screen sharing here going. I’m in full screen mode. Yeah, super excited to be here and talking with everyone today. So my talk is about how to go beyond diversity 101 and really look at intersectionality and how to evoke systemic culture change. And I love going after Lili. I actually have a ton of quotes from the Tech Leavers study in my talk. So if you’ve been hanging in there through the whole thing, you’ll hear a little bit more.

Heidi Williams: So let me talk for a second about why I’m giving this talk. So I am CTO and co-founder of a company called tEQuitable. So we are building a confidential platform to address bias, discrimination, and harassment in the workplace. And really our mission is how to create work culture that works for everyone. So this is everything that we do.

Heidi Williams: And so in the work that we’ve done and with the customers that we have, we’ve learned a lot about what are the approaches that are actually going to work and how can companies really tackle this problem of culture change. We really believe that the same way that 10 or 15 years ago there was not, security wasn’t a big thing and all of a sudden it became a business imperative and people started hiring CSOS and having a board accountability for whether companies were secure, we believe that good work culture is going to be the next business imperative. It’s the biggest threat to our industry if we don’t fix this. And we really, really hope that companies will start taking a strategic approach to fixing their culture.

Heidi Williams: So let’s step back a second and look at what do I mean by diversity 101? So everybody has sort of seen the formula, the stats that said if you have employee diversity, you’ll see increased financial results. And there’s a ton of statistics around this. So if you have gender diverse executive teams, they are 21% more likely to outperform their peers. And if your executive team is ethnically and culturally diverse, it goes up to 33%. And if you have it on your board, you’re 43% more likely to outperform your peers. So the industry got really excited about this idea of diversity and they said, great, let’s go hire us some diversity so we can have better business outcomes.

Heidi Williams: But the reality is that it hasn’t been working very well. And in fact our numbers haven’t changed much. Part of this is related to what Lili was talking about with the Tech Leavers study. We are seeing that white women are leaving tech in mid career at twice the rate of white men, and it’s even worse for people of color. For black and Latinx people, they’re leaving tech at three and a half times the rate of white men.

Heidi Williams: So what is it that we’re doing wrong? The industry took a step back and they, I’m sorry, from that same Tech Leaver study, they saw that 78% of employees were reporting experiencing some form of unfair behavior or treatment and nearly 40% of those employees said that that played a major role in their decision to leave their company. So now we have some data that it’s the fact that people are not being treated well, they’re not being treated fairly. For the most part I don’t think that employees wake up in the morning and see say gee, I can’t wait to be a jerk today. Boy, this is going to be fun. Really, they have behaviors that are impacting others. And either they don’t know that they have this behavior that’s impacting others, or maybe they’re working in a system that is encouraging these bad behaviors that are impacting others.

Heidi Williams: So something was missing here and the industry said great, well we’ve got diversity, but it’s not sticking. So what we need to focus on is inclusion and belonging. And it’s a good thing. It’s something that we definitely need to do, but there’s something in the culture that is causing these problems. There’s something in the system that is causing these problems. And that’s why we’re focusing on those.

Heidi Williams: But at tEQuitable, we really believe that that order of operations was wrong, that people were starting with diversity, they were getting butts in seats, but they weren’t focusing on the culture. And really you have to start with the culture first. Part of the reason the order of operations was wrong is that companies were treating symptoms. They were counting heads. They were saying well, how many women do we have? How many people of color do we have? How many people with disabilities do we have? Instead of taking an engineering mindset and trying to fix the root cause. So my co-founder and I both have been in the tech industry for 25 years and we really believe that we can use technology to solve seemingly intractable problems. And we really like taking an engineering mindset to do this. I’m encouraging all of you to feel inspired by this talk to do the same thing.

Heidi Williams: So let’s talk a little bit about why this was the wrong approach. So for companies treating symptoms, how many of you have heard well, we’re going to start with gender first and then we’ll solve the other problems later? Gender is just easier. But the problem is as engineers, we all know you don’t start with the easy problem. You’re supposed to start with the hardest problem and solve that first because that’s where you’ve got the most unknowns. So that was sort of the number one problem is that they started with the easiest problem.

Heidi Williams: The second thing is that the lack of women in tech is really only one symptom. They weren’t looking at it with this intersectional lens. And we know that if you’re not looking at all of the symptoms, all of the bugs that are happening, you’re not going to find a complete solution to the problem. So that was problem number two.

Heidi Williams: And then problem number really three is that they weren’t asking why women weren’t being successful, why people of color weren’t being successful and thriving. And the first thing that we know about engineering is that you have to ask the five whys. You have to do root cause analysis to figure out what is it that’s causing this bug. Don’t just patch it on the surface. You have to patch it down in the underpinnings and down in the system. So that’s why we really want to help people take a systemic approach to fixing the underpinnings, the culture in which all of the rest of their company operates in order to make real change here. And the study showed that two thirds of tech leavers said that they would have stayed if their employer had fixed its culture.

Heidi Williams: So let’s talk about the tEQuitable formula, which is starting with the supportive culture. So for number one, the supportive culture, let’s start by defining what culture is. Culture is the self sustaining pattern of behavior that determines how things are done. So some people might think it’s your mission or your vision, or it might even be your code of conduct. It might be your values, which you have in lovely posters all over your office. But it turns out that it’s behavior, it’s how you interact with people and how you get your work done that actually determines what your culture is. And so often, even if companies have culture values defined, what’s really happening is that people are, you’re giving the message do as I do and not as I say. So if you aren’t living your values, if your behavior doesn’t exemplify your values, then your values don’t mean much.

Heidi Williams: The second thing is that I want to talk about supportive culture. So supportive culture provides the social and psychological conditions that optimize employee health, safety, and wellbeing. So what do I mean by that? So basically we refer to this as something called psychological safety and it’s something you can see, it’s a Google study that showed data that psychological safety more than anything else is critical to making a team work.

Heidi Williams: And so what is psychological safety? It’s the shared belief held by members of the team that the team is safe for interpersonal risk taking. It also means that as an individual you’re confident that your team won’t embarrass you or reject you or punish you for speaking up. There’s interpersonal trust between team members and mutual respect where people are comfortable being themselves.

Heidi Williams: So let’s talk about that for a second. At your company, do you encourage people to have a growth mindset that they might learn that they can take a risk and it’s okay to fail as long as they learn from it? Is there a culture of forgiveness? Do employees feel heard, they feel like they can bring their whole selves to work or do they have to hold something back? How do you develop trust in each other or empathy or understanding? How do you educate employees about cultural norms or learn about things that they haven’t been exposed to before? Do you have a culture of speaking up? If you see something, say something? Do people feel safe that they can speak up without a risk of retaliation? Do you have a culture of ally ship or advocacy or accomplices where people have each other’s backs? Do you encourage employees to bring their whole selves to work? How do you train managers? How do they do career development? How did they do promotions? How do you educate your employees about how to treat each other well? Really the goal here is that you want to build a community of trust and understanding, of empathy, of communication, vulnerability, and a growth mindset. And if you have psychological safety, then you can really tackle any kind of problem.

Heidi Williams: So let’s talk a little bit about debugging destructive behaviors. Psychological safety may sound like it’s all about the emotions or about the mental aspect of the game. But really it’s the way that you encourage and promote behaviors that reinforce trust and respect and mutual empathy and authenticity and discourage behaviors that tear those down. So the key here is don’t measure how people feel, measure how people behave and the impact it has on others.

Heidi Williams: So to talk for a second about, this is exactly where tEQuitable plays. This is our approach, is that we want to help companies measure their culture and it’s a very complex system. And so the way that we do that is that employees have a way to get advice when something happens so that they can talk one on one with people and have those interpersonal conversations in order to undo behavior on the one on one level. But at the same time, we can gather data company wide and help the company see trends in their culture and in their system and take actions so that they can prevent those things from happening again. So it’s really a virtuous cycle where both employees from the ground up are improving the culture and the leadership and the HR teams are improving culture from the top down.

Heidi Williams: So let me give an example of one of the things that we’ve seen and how we identified behaviors and then talked about the possible systems that might’ve been causing those behaviors. And of course, then what actions you can take to improve those systems.

Heidi Williams: So the example we saw at one company was that interactions between teams were being reported as aggressive and bullying. So you could imagine that those people are just mean, but that’s probably not really the case. There’s some reason why they’re being aggressive and bullying. There’s a bunch of different systemic issues that could be the root cause here. Maybe the two different teams don’t have aligned goals. They have goals that they are totally different from each other. They’re not reliant on each other, and they’re both measured on the success of achieving their own goals. And maybe the success metrics don’t include that you have to collaborate and help others achieve their goals. So you’re only measured on whether your team achieves your own goal. Or maybe the peer feedback system is not part of performance reviews. So there’s actually no way to even report this behavior. So maybe this team that’s being aggressive and bullying has no idea the impact of their behavior on others. So only if you look at the data and then ask the five whys and debug it like an engineer will you get at the possible root causes, the systemic issues that could be causing that behavior.

Heidi Williams: So to talk through that, if you’re going to make systemic change about your supportive culture and create a supportive culture, start by examining behaviors to understand your current culture. Then ask the five whys to find the root cause behind the behavior. And then create systems that encourage psychological safety so that you can promote all of these good things around trust and understanding and empathy and communication and don’t tolerate destructive behaviors.

Heidi Williams: Let’s go on to the next step, which is to encourage create an inclusive workplace. So now you’ve got the supportive culture where people can speak up if you see something, say something, they’re in an environment where they feel safe and we actually are working on belonging first. The second thing is this inclusive workspace. We define it as all employees are valued, respected, accepted, and encouraged to participate across the organization at every level. And active work has done to eliminate all forms of bias, discrimination, and inequity.

Heidi Williams: So let me go back to each of the purple words here, which is that all employees. So the idea is that you want a diverse employee population, that they are valued, and that your definition of value is not narrow. It actually is a diverse and broad definition of values, so that all of the people that are accepted for their differences can express their value in different ways. There’s mutual respect in the organization, that people are respected for their differences and appreciated for their differences, and that they’re encouraged to participate and given opportunities across the organization for different projects and also opportunities for advancement at every level.

Heidi Williams: And then the last piece of this is active work, which is that it requires constant re-analysis. We’ve all seen the things about pay equity that you can’t just fix it once. You have to constantly be looking at it. And it’s not the only thing. You always have to be looking for the next behaviors that need to be debugged and figure out what’s causing them.

Heidi Williams: So let me take a second and talk about intersectionality. And Lili brought this up as well, which is awesome. So intersectionality, the definition is that it’s the complex cumulative way in which the effects of multiple forms of discrimination combine, overlap, and intersect, especially in the experiences of marginalized individuals or groups. Now the reason that this is important, and actually let me just give an example. So if you’re a woman, you might be impacted by the stereotypes and biases of being a woman. But if you’re a woman of color, not only do you have the biases and stereotypes around women, but you also have biases and stereotypes around people of color. And so now all of a sudden you have a double or a triple whammy effect because you’re coming from multiple groups that are marginalized.

Heidi Williams: So the reason that this is important is that when you are tackling exclusion, you really need to need to look at things, you need to dig multiple levels of data deep. So as an example, if you do a survey and you look at what women are saying or what men are saying and you sort of make a broad statement about the women, the reality is because the majority sentiment might be one thing, but the reality is because we have more white women in tech than women of color in tech, the majority voice of women is going to be the voice of white women, primarily. So you have to keep looking at the smaller sets of data to go look at the individual experiences of people who have multiple versions of stereotypes and biases that might be working against them.

Heidi Williams: So let me give an example, another example. So at a company we saw, the percentage of women being promoted was lower than men. We could have stopped there, but instead we dug a little bit deeper and saw that the percentage of non white women being promoted was lower than white women. So now you’ve got two problems and if you stopped at just one, you would have an incomplete solution.

Heidi Williams: So the possible systemic issues that perhaps impacting all women regardless of race and ethnicity is that were no good career development practices and those were impacting women more than they were impacting men. The second one is that maybe the rubric around what leadership looks like is racially biased in some way. If we hadn’t dug into the data to look at all of the “edge” cases or the smaller datasets, we might not have seen that there was an issue around how the rubric for advancement was defined.

Heidi Williams: So I want to also take a moment to just talk about there’s two different kinds of behaviors. The one is the system induced behaviors, which I was talking about, which is that maybe your rubric has a narrow definition and when you measure people against the rubric, people who are different get left behind. The other kind of behaviors we see is not necessarily about the system, but it is that you might have behaviors that stem from a lack of education. I just want to remind that if you go back to step number one, the supportive culture, if you’ve created a space that’s psychologically safe where people feel like they can speak up, they actually will feel like they can tackle these hard conversations that make people uncomfortable. And so now when someone says something that’s offensive or has an impact on you in some way, that’s a stereotype or a bias of some kind, if you’re in a psychologically safe environment, you hopefully feel comfortable speaking up and telling that person what the impact of their words were on them. But I think also that growth mindset means that everyone, regardless if someone has to tell you what you did wrong, everyone with the growth mindset should look for opportunities to learn and educate themselves about how they can be a better ally or accomplice or a coworker in all those ways.

Heidi Williams: So if you want to create systemic change and create an inclusive workplace, measure exclusion and exclusive behaviors. And so one of the things I forgot to mention earlier is that a lot of the ways people measure inclusive workplace today is about engagement. Now engagement will tell you that people feel like they’re engaged four out of five or 80%, but the problem is it’s not telling you about the last 20% and what the problem is. Why weren’t they a five out of five? So if you actually focus on measuring exclusion and exclusive behaviors, that’s where you will be able to ask the questions and do the five whys to dig into what’s going on. So slice that data, look at intersectionality, look at all the edge cases, ask those five whys, and really focusing on what’s the exclusive stuff, what’s missing, who’s not represented, what’s not happening that should be happening. So ask those five whys, find the root causes, and then you can create or repair systems that promote equity and fairness.

Heidi Williams: And the last thing is just educate yourselves. Keep a growth mindset. So again, as a white woman, there’s lots that I can learn about being a woman of color or different sexual identity or ageism or having a disability. There’s always things that even from your lived experience, you can’t forget that other people have different lived experiences and there’s lots of opportunities to educate yourself and make sure that you’re not contributing to the problem.

Heidi Williams: So hopefully when you put all of this work in place, you’ve created a supportive culture, you’ve created an inclusive workplace, the last step should come really easily. Who wouldn’t want to work at a company like that? You’ve created a place where people can, if they see something, say something, you’ve created an equitable opportunities for everyone to advance and pretty soon you should see diversity across your organization.

Heidi Williams: So the main thing I want to leave you with is that I would really like companies to not take a diversity strategy, but take a culture strategy and really make it a critical aspect of what they’re doing and how they’re going to succeed. And if you take an engineering mindset, just go through the math a little bit, if behavior is culture then behavior change means culture change. In order to affect behavior change, you can look at the buggy systems that induce those bad behaviors. You can look at the supportive culture that perpetuates those bad behaviors, but if you use an engineering mindset and debug your culture, like an engineer with an intersectional lens, you can really create systemic change in your organization and create a culture where everyone can bring their whole and best selves to work. Thank you.

Angie Chang: Thank you, Heidi. That was a very inspiring talk. I see lots of chatter and questions, but unfortunately we’ve run out of time. So thank you so much. If you can share your slides, I’ve seen a lot of questions about sharing your slides. Are you hiring? Are you going to make these findings public? But we have to get on to our next speaker, so thank you so much and we will be back shortly with our next session.

Heidi Williams: Thank you.

Like what you see here? Our mission-aligned Girl Geek X partners are hiring!

• See open jobs at Atlassian and check out open jobs at our trusted partner companies.
• Does your company want to sponsor a Girl Geek Dinner in 2021? Talk to us!

Atlassian girl geeks: Dominique Ward, Lori Kaplan, Ashley Faus, Ritika Nanda and Aubrey Blanche answer questions about interviewing and ageism from the audience at Atlassian Girl Geek Dinner in San Francisco, California.

Speakers:
Aubrey Blanche / Global Head of Diversity & Belonging / Atlassian
Ritika Nanda / Mobile Developer / Atlassian
Ashley Faus / Senior Manager, Integrated Media / Atlassian
Lori Kaplan / Head of Design, Cloud Migrations / Atlassian
Dominique Ward / Design Operations Lead / Atlassian

Transcript of Atlassian Girl Geek Dinner – Lightning Talks & Panel:

Global Head of Diversity & Belonging Aubrey Blanche gives a talk on “Thank u, next: How “diversity” gets in the way of gender equity” at Atlassian Girl Geek Dinner.

Aubrey Blanche: Welcome everyone to our still semi new space We moved in here in November and let me tell you, these view is better than a warehouse in SOMA. I walk in everyday and I’m like, “Don’t get entitled. Don’t get entitled.” It’s so gorgeous. Our team does such a beautiful job with the offices.

Aubrey Blanche: I am Aubrey, I am Atlassian’s Global Head of Diversity and Belonging. If you’re like my dad and you’re like, “What the hell does that mean?” Basically I think of it as my job to help Atlassian hire the right people and then make sure that they’re treated fairly and that they can thrive while they’re here. That’s what I am here to talk to you about today, is the way that we are thinking about designing a structurally equitable company. Then you will have the absolute pleasure of listening to some amazing panelists who are all at Atlassian building amazing things. You’ll have me for about 20 minutes and then we will hand it over to them.

Aubrey Blanche: First of all as you can tell, I am an Ariana Grande fan. What I am not a fan of is diversity. Everyone’s like, “You can’t say that in 2019.” The answer is actually I can. Fuck diversity, and the reason I say that is because the word is a problem because it, one, doesn’t represent our goals and actually getting in the way of us individually and companies making progress. I’m going to talk about why that is. Before we get into what Atlassian is doing to crush the kyriarchy with capitalism, what I want to talk about is where we are today.

Aubrey Blanche: Every year Atlassian does a globally representative survey called the state of diversity report. We started this a couple of years ago because I couldn’t find good benchmarking data about attitudes and behaviors towards what was then called diversity and inclusion in the tech industry. So what did we do? We collected it. We’re big on open, so we shared it. This is what the 2018 data showed us.

Aubrey Blanche: Oh I’m supposed to tell you about Atlassian first. I’m bad at the sales pitch. At Atlassian, if you’re not aware of what we do, if you’ve played with JIRA, we make that. We also make a wealth of other collaboration productivity software. Things like Trello, Confluence, Bitbucket. Our mission is to unleash the potential in every team. As you can imagine that makes it pretty easy to justify my job, because every team is made up of people from an incredibly broad set of experiences and backgrounds. We want to make sure that we’re building for all of them. Now what does the world look like?

Aubrey Blanche: The 2018 state of diversity report showed that good news, companies are saying that diversity is important. Here’s a fun fact. Retention belonging is not up. People are saying it’s important, but there’s not even movement in a positive direction and sometimes we even see backpedaling. That’s what we see at the corporate level. From 2017 to 2018 we saw a 10 percentage point drop in companies that have formal diversity and inclusion programs.

Aubrey Blanche: I’m here to tell you that saying that you care about something is not the same thing as caring about it. Saying it’s a priority and doing nothing is simply complicity and a mediocrity that the industry has enjoyed for the last couple of decades. What was especially disheartening to me about this was that, individual engagement with diversity and inclusion programs was down in some cases 50% year over year. We dug into the comments and we asked why. It turns out that one of the blockers was the fact that we are using the word diversity. Who here has ever been called a diverse candidate? You were lied to, diversity is a group construct. You cannot be diverse by yourself.

Aubrey Blanche: Most people think that this is my job. That is not my job, this is my job. I build balanced teams. I reject the word diversity because the state of diversity report showed that that word only means something for two groups, white women and black Americans. Literally in Australia, Australians were more likely to say that African Americans were diverse than indigenous Australians. It’s weird. Also, I’m here to tell you that black people aren’t diverse. They are minoritized. They’re under represented. They’re under resourced, but they’re not diverse.

Aubrey Blanche: What we did is we shifted to talking about building balanced teams. Why does this work? It works because, first of all, who is going to die on the hill of building an imbalanced team? Right, no one. The second reason is because it’s taking that word that means something we don’t mean, something too narrow and actually allows people from different types of groups in. What if you’re Latina like me? What if you’re queer like me? What if you have multi disabilities like me? What if you’re over 40 like me eventually? The point is that we were cutting people who were truly marginalized in ways that we were creating no space for. What about that straight, white man who grew up in a trailer park, who is facing many of the same social challenges that people from visible minoritized groups face? They see the programs as pretty hypocritical and frankly that’s fair. Even though they may have experienced structural privilege because of their visible characteristics, they still deserve support and a voice for those things that they need support for.

Aubrey Blanche: What we found is that it also — I think the critique that I get is, this is obscuring the racial challenges that we have in the industry. What I found is the opposite. At Atlassian as we’ve moved to this language that is less charged, we are actually now able as a culture, as a company to have more direct conversations about race and specifically about anti blackness in tech. I think that’s pretty cool. Now I think it’s a reasonable question to say, “Yeah, but how do you do that?” It’s not just a branding thing, this is just not brand repositioning. This is actually about structurally designing the organization to be fair and equitable for everyone.

Aubrey Blanche: I really believe that the entire field needs a makeover if we actually want to make true progress. In this spirit of Ariana, this is not a diss track to my ex, this is a thank you so much for that learning experience, but it is time for something new. We need to move away from diversity, which has a limited meaning and actually is not aligned with the goals that we’re trying to build. We need to build balance in our organizations.

Aubrey Blanche: We also need to move away from inclusion. Inclusion assumes that I can fit like an add-on into a power structure that was built for straight, white men. I have no interest in doing that. I’m not any of those things and I don’t know how to show up that way. I want to actually build belonging. I want to show up in a space where I was considered and where I was thought of. That doesn’t mean — it can be the littlest things that show me that. You’ll see here, research shows that women feel like they belong when there’s more plants in an office. You’ll see that our bathrooms, even the ones that because of building codes have to have gendered words on them, do not actually contain pictures of what a man or a woman looks like. That might not matter to a lot of you, but to folks who are gender nonconforming or non-binary or transgender that has huge meaning. That little subtle clue actually tells their brain that they belong in that space. That’s what we’re trying to build at Atlassian and I think we can all resonate with wanting to feel like we belong.

Aubrey Blanche: I’m really, really over the branded PR version of diversity where it’s like, look, we got intersectional feminism cupcakes. Did nothing else but put a photo on Instagram. It’s not good enough. I’m super pro intersectional feminism cupcakes, for the record, we had for International Women’s Day. We just did other things, too. I think it’s time that we design the organization in a way that is structurally equitable for everyone. We need to stop thinking that women equals diversity and embrace an intersectionality for strategy. How does this look like?

Aubrey Blanche: It’s pretty simple. If I think of someone who has inter-sectionally marginalized identities, let’s say myself. If I as a queer Latina woman can succeed in the organization, any changes that I’ve made are definitely going to benefit straight, white women, too. When we start diversity equals women, we only build programs, processes, and structures that help straight, white economically privileged women succeed. Who certainly face barriers compared to their male counterparts, but we end up further marginalizing anyone who doesn’t fit that bucket. I genuinely, genuinely believe that we can all win together, this does not have to be a competition.

Aubrey Blanche: The last is, we have to stop focusing at the company level. I will give you some data later at the company level because it’s more meaningful to you. At Atlassian we actually look at and report on balance at the team level. You can keep me honest, it’s on the website atlassian.com/belonging. You can even look at our subjective belonging data if you like. The reason for that is because that feeling of belonging and feeling like your value doesn’t happen at the company level. People don’t stay at a company because of the employee resource group. They stay because on their team their expertise is valued and used. We believe that if we give teams the power to create belonging and to see and value and respect people’s opinions, that’s why people will stay. They will stay, they will do great things. Our customers will be thrilled. The stock price will go up, it will be awesome for everyone.

Aubrey Blanche: Now how are we actually doing that? First we just measure everything. Monthly I look at hiring rates and at minimum we ensure that we are hiring at what we call marketing availability. Meaning there is no excuse to be hiring at lower than is available on the market. That’s of course a variety of metrics, gender, race for our US offices. We’re primarily an internationally based company. We look at age and we’re looking at getting a better ability to collect other data like disability and veteran status. We look at promotion velocity, so not just making sure that you’re coming in, but are you actually being promoted at equitable rates to your peers? We look at that by group, by remote versus in office, and also pay equity as it makes sense. Just pay people fairly. It’s a good idea. You don’t get sued and everyone feels valued. Like it’s a bad idea to pay people inequitably.

Aubrey Blanche: Inclusion. Every — annually we measure three things in our engagement survey. I feel like I belong on my team. I can be myself at work. My team has diverse perspectives that influence our decision making. We don’t want people to bring their whole selves to work, because that’s some weird boundary violations I don’t want to get into or you might have some shady opinions that are not welcome here. Right? It’s weird, like your whole self. I don’t bring my whole self, but my authentic self I do. That means I get to pick and choose what I bring in.

Aubrey Blanche: Then we wanted that diverse perspectives question, because it’s not just about feeling good and being in the room. It’s about your opinions actually making it, not just into the room, but being used to influence what’s happening in the company. That tells me whether people’s opinions are valued. The last is nutrition. It’s a lagging indicator, but if there’s a huge gap, if some marginalized or under represented group is running for the hills, like that’s a good sign that something is busted. We look at and we monitor all of that really closely and that’s how I think about where I prioritize for invest.

Aubrey Blanche: What does that look like programmatically? Everyone always asks me for like the silver bullet and I’m like there are none. There’s like 500,000 really tiny ones. You need to basically rip out everything about a company and put it back in. Our recruiting team, just in the last couple of years, has developed sourcing libraries. We literally have lists of hashtags, sororities and fraternities, minority serving institutions, professional organizations. We can find under represented people on the internet. What we’re trying to do is solve for the nonnegotiable trade-off between time and people who are numerically rarer. We also use structured behavioral interviewing, so we don’t ask you questions like how many golf balls fit in a 747 because it turns out that doesn’t tell you anything about a person. We ask the same questions in each interview because it is very helpful to compare skills when you ask the same questions of all candidates. It also gets rid of bias.

Aubrey Blanche: We removed culture fit from our hiring and we talk about values alignment. In that interview we look for specific behaviors and qualities that are both predictive of the culture that we want to build, but completely agnostic to your background. The fact is, you can learn how to make really effective trade-offs whether you’re running a global P&L function or you’re just getting your kids to soccer and dance and getting dinner on the table. I definitely don’t care how you got that skill.

Aubrey Blanche: We also look at the balance of our interview panels. We’re right now actually benchmarking all of that to ensure that by the end of the calendar year no candidate that comes to Atlassian will meet an all all-male panel. We’re looking at women and non-binary focus as our first measure of balance and then we’ll evolve our approach from there. We use the balanced slate approach, so this is a team focused way that we ensure that we have under represented candidates in consideration for our most senior roles. We have strategic partnerships with organizations like Girl Geeks, where we get to meet incredible people hopefully that want to be on the team now or someday. We have events and meetups like this and we also do what I am calling impact brand activations, which is a really good way to say Austin’s awesome ideas.

Aubrey Blanche: A story about this, I didn’t tell you I was going to do this. At Grace Hopper last year we had a swag budget just like everybody does. Austin was like, “I have this idea.” Austin wanted to do something that was a little bit more Atlassian, so we’re big on philanthropy. Our foundation is focused on access to education. Austin actually created a giant JIRA board that said, what is it, “Women Who Code, Code2040, Black Girls Code.” Instead of getting a t-shirt that definitely is not going to make you have a job here, how many have ever taken a job because you got a t-shirt? We actually gave everyone who came to our booth two stickers. One was for them to keep and one was for them to put under the name of the organization that they wanted us to donate their t-shirt budget to.

Aubrey Blanche: Why is that awesome? Well, first of all, it helps us identify who’s actually going to make a great Atlassian, right? If you’re pissed you didn’t get the t-shirt you’re not a values aligned person anyway. We’re so big on philanthropy that it attracted people who were attracted to that culture and we created more access for women in tech. What we did at the end was we counted the stickers and the proportion of stickers we donated the budget. That was just really fun. These are the things that any company can do. No one needs to print t-shirts, it’s not a green thing to do anyway. It’s also important to think about the experience that people have once they get here. A lot of people think that this is a recruiting problem, and I will tell you it’s a culture problem.

Aubrey Blanche: This is an example of how we build gender equitable processes. Last year at Atlassian we completely overhauled the performance assessment process. I know, everyone’s favorite time of year. What we did was we ripped it down to the studs and we said, “What could we do to make this as equitable as possible?” Traditional performance assessment, you’ve probably had one, the question’s basically, how well did you do at your job? Did you hit your goals? It turns out that that doesn’t take into account the way that you show up, the behaviors that you exhibit in the workplace. It certainly doesn’t take into account all of the office housework and emotional labor that we all do all day. What we did was we actually leveraged experimental testing and broke the assessment into three pieces. Now there are three equally weighted pieces of your performance assessment at Atlassian. There is values, which actually has a list of values aligned behaviors. You can get a pass/fail. Then there is role, what did you do? We created a new component called team. The question here because we are the team company was, what have you done to benefit your team?

Aubrey Blanche: This could be, did you volunteer for a balance and belonging initiative? Are you just that person who’s always going the extra mile to help onboard people? Are you the one who’s organizing lunch? All of these things count as team contributions and we wanted to create a way for people who do those things to get credit. I happen to know that underrepresented people do more of that work. The fact is, it should be rewarded in the same way that writing great code should.

Aubrey Blanche: The great thing is that, these things are equally weighted in your assessment. How well do you think the brilliant asshole’s gonna to do in that? Not very well and that was intentional, because we want to create well rounded people. We also found that by rating each component separately it reduced the halo effect. That meant like if you are great at technical role, you would get a bump on values or team. What we have our managers do is actually rate each component separately and then an algorithm gives them the recommended rating. We have a logic for that too. If you get low in any category you get low, for example.

Aubrey Blanche: We also use Text.io to get rid of the gendered language that could creep into the assessment. We know that agentic language is more male gendered and communal language is more female gendered. We remove gender language at all so we’re not building that into the structure of the system. Last, we named our performance levels using a growth mindset framework. What it says is that companies that have growth mindset cultures rely less on stereotypes in evaluating people. Which means that they are less likely to make biased and discriminatory decisions. At Atlassian you can’t get a legendary, you used to be able to. Now you can have an off year, a great year, or you can have an exceptional year. The reason is also because we don’t want to label you as a person, we’re talking about how you’ve done in the last year. Maybe you had an off year because you had a crazy family situation. We’ve all been there, but we believe that you can always improve and that we’re just giving you a check in on your performance.

Aubrey Blanche: All of these things together we combined with a live audit of the system. We actually audit our performance scores before they’re locked to make sure that there’s no gaps, but from a demographic point of view or differences. So that when Atlassians get their scores, they can be confident that we have checked to make sure that there’s not any preventable bias in that.

Aubrey Blanche: Over the last four years we’ve increased by nine percentage points our women in technical roles. Which is pretty phenomenal when you think about the fact that less than 13% of CS degrees in Australia are given to women. That’s our largest engineering center. We’ve also almost quadrupled in size during that time. That’s really exciting because like two weeks ago that number was 8.4 and I had to update my slide this morning. Yes. It’s the best mistake. That is looking at our technical roles, so we’re very R&D heavy company, so that’s probably about 75% of our org. Overall 30% of employees at Atlassian globally identify as women. Now what I bet you’re probably all thinking is, “Yeah, but are they all entry level employees?” That’s usually what happens, right? We don’t see representation at the senior levels in the same way we do at more junior levels. I think the thing that means the most to me is that our senior level representation is actually leading by a little bit. I think that that’s been one of the keys towards growing representational over all is that we’ve hired fantastic underrepresented leaders. Which means that people feel more comfortable coming in because they can see what their career looks like. If you can’t tell I’m generally a perfectionist and never satisfied with anything.

Aubrey Blanche: There’s a lot more work to do. We don’t want to pretend like we’re perfect, but we do make an active effort here and really want to be seen as someone who’s putting a lot of effort and time and thought into it. We know this is an unsolved problem, we won’t know all the answers, but we will share. We hope also that you will take some of this, make it better, and then come back and tell us how to improve. What I’m thinking about right now is building more communities. We don’t have formal ERGs at Atlassian. We actually allow communities to form and then we support them. We are doing a little bit more strategic investment especially looking at our black Atlassians this year, who last year our data showed that they’re having a very different experience. Even than my other underrepresented racial groups in the US so that for us is really important to say, “Nope, that’s not okay. If you’re reporting a problem we’re going to solve it for you.”

Aubrey Blanche: We’re also looking at meetings. We believe that if you can just improve the quality of how people feel valued and brought into meetings that we will meaningfully change their work experience. We’re studying what inclusive practices are happening and figuring out ways to nudge people into more inclusive behaviors. The last is, we’re actually talking about open dialogue. One of the models we use at Atlassian is what I call open source education. We have an internal blog. We encourage everybody to use the blogging confluence, but side by side where individuals write about their own histories, their own experiences, and how that impacts them at work with a specific focus on helping their teammates understand how to better support people like them.

Aubrey Blanche: We had one of our principle developers in Sydney write a blog called, “How not to fuck up with your trans teammate.” She wrote that from the spirit of, there’s a lot of really wonderful people who don’t want to do the wrong thing and so they do nothing. She wrote about her experience and at the end it was like, “Do these five things. Definitely don’t do these.” I wrote a blog a couple of months ago because we have a lot of non-American folks. At the end it was like, what does black mean? Can I say it? The fact is that a lot of people have those questions but are terrified to ask them, and so they just run away from people. What we really focus on is creating a space where it’s okay to ask questions to learn about how to be more inclusive, which I think is powerful, because that doesn’t come from me, that comes from our employees. Again, it’s so much more motivating to have someone on your team, on your team say, “You should do this.”

Aubrey Blanche: I just had an engineering manager this morning find a blog about new ways to share your pronouns because we just rolled out a pronoun field in Slack and how to do it in your email signature. He’s like, “Oh, why didn’t you share this to the whole company?” I was like, “Oh, because people are just sharing it with their teams.” He went and sent it to all of the bucket. I think that’s really powerful because I started seeing all these new things pop up in email signatures, but it wasn’t because I did it. It was because people are motivated and they create the community, so they feel bought in and they did it. Thank you so much for listening to me, but now I want to give you a real treat and invite up our fantastic panel to talk about their experiences. Hi, and that is my dog. He’s very codependent.

Atlassian girl geeks: Lori Kaplan, Ashley Faus, Ritika Nanda and Aubrey Blanche at Atlassian Girl Geek Dinner.

Aubrey Blanche: All right so the panel knows this. We have an incredible group of Atlassians from different groups, from different sort of career experiences. Instead of having me introduce them, which would be far less interesting, I’ll have them introduce themselves, so are the mics on? It’s all good?

Ritika Nanda: It’s good.

Aubrey Blanche: Why don’t you give us your name, maybe your role, your pronouns for us. Then what identities are you carrying with you today?

Ritika Nanda: Okay. Mic check, sounds good. Okay, so I’m Ritika, I’ve been working with Atlassian for about two and a half years. I like to be identified as she or her and I would say very briefly I am a programmer by profession and an animal lover by heart. I guess that’s about it. Yeah, but I would definitely do want to say all what Aubrey said about Atlassian, having values really ingrained in all the employees is absolutely 100% true.

Aubrey Blanche: Yes.

Ashley Faus: Good job marketing as the marketer. I’m Ashley, I’m a marketer, writer, and speaker by day and then a singer, actor, fitness fiend by night. Pronouns are she and her. Here at Atlassian I do a mix of content, social media strategy, and the intersection of where all those meet at various groups at Atlassian.

Lori Kaplan: Hi, I’m Lori. I like she and her. I’m the head of cloud migrations and by our experience design and content here at Atlassian. I’ve been here about 18 months. I don’t know who that was that was a shout out in the back. Oh, it’s on my team. Yay, team.

Lori Kaplan: I know and I’m a native San Franciscan, proud mom of two young adults and a two year old Goldendoodle, who sometimes comes to work with me. An avid hiker and a reader.

Dominique Ward: My name is Dominique Ward. My pronouns are she, her. I’m design operations lead here at Atlassian, which basically means I have the very meta role of helping to enable and unleash the potential of our design team who then in turn design products that help unleash the potential for our customers. I identify as a black gay lady, lady, very specifically. A New Yorker, new San Francisco person, but New Yorker. A systems nerd and all around nerd. A Barbra Streisand and 90s hiphop and R&B devotee. All of those identities and more wrapped up in the identity of being a zen practitioner.

Aubrey Blanche: That’s awesome, that was like the best intersectionality discussion I’ve ever heard. Also want to tell you Spotify made a great throwback Thursdays women of 90s hiphop playlist for me today.

Dominique Ward: Was there Destiny’s Child though?

Aubrey Blanche: No, that was 2000s.

Dominique Ward: That was 2000s?

Aubrey Blanche: Yeah. This is like Eve, it’s great. Thank you, welcome. Thank you for being here. Being successful in your career, as much as I hate taking the negative, is — often involves overcoming some kind of a challenge to get to the level that you’re at. I’m curious if any of you can tell me about a time when you ran into a roadblock or ran into something and what you actually did to weave around it.

Ritika Nanda: Okay. All right, I can start. Okay this reminds me of one thing, I’ve been lucky I’ve worked with really good people all my life. It does remind me of one thing. I did my undergrad as a mechanical engineer, all right that already hints towards a few things. I was probably amongst the two girls in the entire class, fine, I had a lot of fun. Finished my four years of undergrad, but then wanted to have a job. I tried to look for a job, but then at every single company I sat, I tried to get a job, they either didn’t want to entertain female candidates. Or it was a subtle non-written rule that, fine, you can come and sit, but we’ll probably are going to just hire male candidates. I really wanted to get a job. I’ve got to earn money do something in my life.

Ritika Nanda: Then I thought, “What should I do?” Then as I said, I studied mechanical engineering, so there were a few courses which taught me how to write a few programs, run a few CNC machines. Then I thought, all right that sounds cool, that was fun, I enjoyed it. I learned a few programming languages and since then I’ve been coding. I guess I think that you should never think you cannot do a certain thing. That’s the job of the rest of the world, let them think that you cannot do a certain thing. You can always do whatever you want to do. I guess that was a good example I could think of in my life up to now. Yeah.

Ashley Faus: Mine was thinking about how to choose among, I was really fortunate to have a couple of job offers in my previous round of interviewing. I basically looked at ,okay, what do I want to do in the next 10 years and where does my skill set map to that over the next 10 years? I basically realized that I had a gap in my skill set.

Ashley Faus: One of the job offers on the table would fill that gap. It was going to be a really stretch role for me. I was probably going to fail when I walked in because it was a huge gap in my skill set. Then the other job was something where it’s like, oh I can hit the ground running. I’m going to knock this out of the park. I’m going to come in and six months later they’re going to be like, “Oh my gosh, I’m so glad we hired this person because she knows what’s up.”

Senior Manager of Integrated Media Ashley Faus shares her career journey at Atlassian Girl Geek Dinner.

Ashley Faus: As I was weighing those like it’s really tempting to go toward the comfortable job and it’s really tempting to go to the place where you’re going to be most successful. But if you look down the road and you say, I want to be a VP or I want to be a CMO or I want to be a manager or a dev lead, whatever it is, where are the gaps in your skillset and take an honest inventory. Be honest with yourself to say, “I kind of suck at this one thing and this person is kind enough to give me a paid learning opportunity and knows that I’m probably going to fail. They’re willing to walk with me to teach me how to do this because they think I have potential.” Go toward the thing that’s going to stretch yourself.

Ashley Faus: I ended up taking that job and it’s interesting because my manager and I had several one on ones where both of us were identifying that I was like veering toward the place where I was most comfortable. We both had to work really hard to keep me focused on the skillset that I needed work on. That would be my thing, is just encourage you to take the job that helps you build the skill gap. Take an honest look at where those gaps are for where you want to go in the future.

Aubrey Blanche: Lori, I know you weren’t always in design right?

Lori Kaplan: I know.

Aubrey Blanche: You eventually made a switch.

Lori Kaplan: That’s a philosophical debate, but [crosstalk].

Aubrey Blanche: I mean we can do that, I have space for it.

Lori Kaplan: What?

Aubrey Blanche: I said we can do that, I’ll hold space for that.

Lori Kaplan: Thank you. My first tech role was — my title was technical writer. I did that for a long time and many of the jobs involved design of some sort or another, but I was kind of pigeon-holed into a certain thing doing guidelines at a really cool company. I had hit a wall in my career growth and I thought, “You know what I’m I going to do about this? I’m only getting the same kind of opportunities here.” I wanted to do something that challenged myself, grew my skills, and move into an area I was really passionate about which was interaction design at the time. Now we call it UX or product design or design whatever we call it.

Lori Kaplan: I put together a little portfolio and I started talking in my company about what other opportunities might there be and was there openness to my shifting roles? It turned out that there were. I did have to go through the same interview process as the other candidates coming in from the outside. I ended up getting an offer there, but at the same time, all my friends were going to a new cool company called Netscape. I don’t know who’s here is old enough to remember that. I thought, “Oh man, that looks really awesome.” That’s a really a growth opportunity, so I interviewed there and got a role there. That started me on this path of being a designer.

Dominique Ward: I had a similar experience, well multiple. I feel like over the course of my career it’s been the periods where I can either go this way or I can go into a completely new direction. There have been many moments where I have that — I think the first one that comes to mind is me kind of being an analyst at heart. I got a job that I wasn’t supposed to get to begin with, didn’t even apply for. Then that took me to a design consultancy where I was very purely an analyst on a program management team.

Dominique Ward: There was a shift in the org and someone asked me, “Would you be interested in shifting teams?” I said, “Sure.” I have no idea what I’m doing, but this is the opportunity that ended up propelling me into a new role. I got a bird’s eye view of what it took to actually build and design products that were going into the market and how that ended up impacting a global organization. That was something that I had no idea that I wanted to do. All I wanted to do was work in museums or maybe be a philosopher and now I’m here.

Aubrey Blanche: That’s great. I have multiple degrees in political science. Very useful. A lot of what we’ve talked now is about your individual choices, but I think most of us who’ve been in the workforce know that how important the role of your manager is and what that relationship looks like. Lori, obviously your team is giant fans of you. I mean for good reason. I would love for, if you could talk a little bit, maybe you can speak to as a little bit of a transition how you realized that you wanted to have a management role. I think sometimes people don’t realize that senior level ICs is also a great path. Then maybe how do you think about what your role is in relation to your team?

Lori Kaplan: Okay. The first management job I had was when I was in college, but that was a really long time ago and it was in a retail setting. I learned from that that it was really hard to manage other people to performance. I was, I think, probably too young. Anyway, fast forward a few years in tech and I went to another really cool company called Netflix and my boss was taking a leave, a personal leave of absence. She and her boss decided that I should do the job temporarily. I said, “No, I’m really having fun.” They said, “Please.” Anyway, it went back and forth and I said no a lot. Then they begged and I said, “Okay, but only until Nancy comes back, promise?” They said, “Yes.”

Lori Kaplan: Well it turns out that once I was in the role I discovered I really loved it. I was pretty good at it, although I did have a lot of growth and a lot of gap in my skill, so I’ve gotten a lot of coaching along the way. I think this is pretty typical of mid career people of bouncing back and forth between people management and IC, because I did miss the craft. I think that’s really hard when you’re in a craft role. Then in the last probably 10, 15 years I’ve been only a people manager.

Lori Kaplan: How I think about my role is, I am there to be the servant leader of my team and really in service of them doing their best work. Helping them understand where their strengths are, where their opportunities are, where their challenges are. Identifying how we can focus on an area they need growth and how we can align their work with their growth needs. Leverage all of the opportunities and support that we have especially here at Atlassian. I make that a regular part of one on ones with my team. We also do growth plans here. It’s a regular part — we’re held accountable for are we having those conversations; we get gentle nudges from our people team. As part of our evaluations we’re held responsible for our teams — at least my boss does, “Is my team growing?”

Aubrey Blanche: Yeah, and I think you have such a great story and most people don’t realize they know that they can go to their manager for a career conversations and things like that. One of the challenges I see is that people don’t know that they can go to their manager just to get some support or to bounce ideas off of to help with collaboration situations. I think you have such a great story around how that shows up.

Ritika Nanda: Sure. I can share a story and I think a couple of folks in the room might be able to relate to that. Being a programmer in Silicon Valley, it’s not a very uncommon situation when you are the only lady in the room and everybody else is a man or is a male. I feel that’s great at least to diversity or it leads to more balance in the team I would want to say that [crosstalk].

Aubrey Blanche: We’re making it happen like fetch.

Ritika Nanda: As soon as I said diversity I looked at her, balance team, I guess.

Aubrey Blanche: I’ll keep you no matter what.

Ritika Nanda: Yeah, thank you for that. What that leads to is just generally a difference in the way you express your thoughts. Since you are a minority section in the team it might be different than the rest of the team. It’s after all managers and everybody is just a human being. It might be possible that since it was different they didn’t recognize it. I have been in such a situation. I’m sure a lot of other folks in the room must have been in that situation. My really self [inaudible] advice is that, talking really helps. If you go talk to your manager and tell him or her that this is, “Hey this is what is happening and maybe I am getting overshadowed in this area, just because my way of expression is different than the rest of the team,” so that really helps. It worked great for me and I would really recommend and suggest everybody to do that if you’re ever in that situation. That’s a high level of my story.

Aubrey Blanche: Yeah, and I think — because we like to keep it real here — is, sometimes there’s also where I think we’ve obviously great managers who show up. Sometimes managers aren’t the right support structure for you and I think it’s also okay to advocate for yourself in that situation. I don’t know if you want to share — pass the mic all the way down — about what that might look like.

Design Operations Lead Dominique Ward gives career advice at Atlassian Girl Geek Dinner: “Choosing yourself can be an empowering thing. If there is a situation that’s not serving you, then it’s also okay to respectfully say, ‘Thank you, next.”

Dominique Ward: I have been very fortunate to have really great managers, mentors and, advocates over the course of my career and have seen the positive impact that’s had to my career development and my career trajectory. When I was in the position of actually having an issue with the way that I interacted with my manager and their lack of interest in my career development, I then had to make the decision of — do I go above them?

Dominique Ward: I went to HR and also had a very direct conversation with my manager. After a few months nothing changed and so then I had to make the decision, is this a place where I want to stay? Then start to harbor distaste for the company that I’m in, the role that I’m in and both that I love? Or do I move forward and try to make a fresh start? I decided to leave and that was really difficult for me, but ultimately it was the right decision to make.

Aubrey Blanche: Yeah, I mean I think that choosing yourself, right, can be an empowering thing. If there is a situation that’s not serving you, then it’s also okay to respectfully say, “Thank you, next.”

Dominique Ward: Next.

Aubrey Blanche: I want to leave a little bit of time for Q&A for folks here who would want to ask this brilliant panel questions. If we could go and I will start with Dominique since you already have the mic, your quick tip. If there was one thing that you could tell people that they should do, not just to be successful in their careers, but to be successful in their careers as who they are, what would you tell them?

Dominique Ward: I would say advocate for others and find people who can advocate for you. That doesn’t mean necessarily someone who’s above you or more senior to you. It could also be your peers, someone more junior, someone who you can bring their name into a room when they’re not there and say, “Actually, you should talk to that person,” and then other people will do the same for you.

Head of Design for Cloud Migrations Lori Kaplan shares advice at Atlassian Girl Geek Dinner: “Be crystal clear on the unique thing that you bring to the table. Don’t get slotted into a box that you didn’t choose.”

Lori Kaplan: I’m going to borrow from one of my favorite authors Anne Lamott who talks about radical self care. Look it up, it’s very important, but the baseline is, we give so much. We do the extra emotional labor. We show up in so many ways in all the identities we have. If you don’t refill your tank and take care of yourself, you can’t continue to do that with the same level of vitality and impact.

Ashley Faus: I would say mine is to be crystal clear on the unique thing that you bring to the table. Don’t get slotted into a box that you didn’t choose. Choose your own box and be very clear on why you chose it, how you chose it, what you want to do once you get there. Just be very clear about that anytime you’re dealing with career progression, interviews, those kinds of things.

Mobile Developer Ritika Nanda shares a mantra which has worked for her at Atlassian Girl Geek Dinner: “Don’t be scared, it’s fine. If you see something is a stretch, go for it. Somehow you’ll make yourself adjust and reach that goal. That has worked well so far for me. I hope it keeps going that way.”

Ritika Nanda: Gosh, it’s hard, after so many good suggestions I don’t know if I have that great insight for the suggestions. I’ll try. I think the mantra which has worked for me is that, don’t be scared, it’s fine. If you see something is a stretch, go for it. Somehow you’ll make yourself adjust and reach that goal. That has worked well so far for me. I hope it keeps going that way.

Aubrey Blanche: I mean I would say so. My advice is find your squad. I am the DNB team here at Atlassian. I think one of the things that makes me so happy is not only that I have my squad here, right, folks who help me do the work, but also will be like, “I need a walk.” We’ve all had that day at work and I try to solve structural racism, so as you can imagine that’s easy.

Aubrey Blanche: I also have a community outside of work. We have what we call empathy wine, with a H in parenthesis. I think that’s important to have both of those, is your squad at work who’s going to get your contexts and be able to help you move whatever your goals are forward. Also knowing that having your squad outside of work is incredibly important. I think that is part of radical self care, is making sure that there is always people that you can reach out to. That you actually do it. Please do that. Do as I say not as I do. Yeah, know that your squad is there and pick people who show up when you need them.

Aubrey Blanche: Yeah, all right. We have about 10 minutes left technically and I did not plan who is going to run the mic around. We have wonderful Atlassians here. If anyone has questions.

Shauna: We need a mic.

Aubrey Blanche: Yeah, do we have another one, another mic?

Shauna: I need an actual mic.

Aubrey Blanche: It’s cool, it’s a team effort, we’ll just share.

Emily: Hi, I’m Emily. I have a question about how you bring your authentic self to work, so maybe just discuss about what you do choose to bring to work. You don’t have to tell us what you don’t. I think like what’s something that makes you feel like you are you even at the office?

Ashley Faus: Dominique’s pointing at me for some reason. I’m a lot, I straight up told Aubrey I was like, “Just be aware I’m going to try to tone it down.” I’m extremely extroverted. I walk really fast, I talk really fast. One thing that I try to do is to match the energy with the people that I’m working with. I’ve had situations where — and it’s not like a woman thing that it’s like, “Oh you’re aggressive.” It’s like, “No, you’re just a lot for everyone.”

Ashley Faus: I also do musical theater in as I said singer, actor, fitness fiend. Just the amount of expression and talking with my hands and loud voice that comes across, sometimes that can be intimidating to people. I try really hard to make sure, particularly if I’m managing a team with direct reports, like smile, swing by their desk, talk to them so that every time I come by they’re not like, “Oh my gosh, something is wrong.” I do try to intentionally walk slower and smile at people so that they aren’t just like, “Oh, she is on a mission and we are not to speak to her.” It’s like, no, no, you can speak to me, this is how I walk. Recognizing those things about myself and trying to mitigate them, not because they’re bad, just because I don’t want people to feel like they can’t approach me. That’s my story of that [inaudible].

Dominique Ward: I just wanted to hear you speak more.

Aubrey Blanche: I would say I’ve got a weird answer. If you googled a photo of me from like two years ago I would have looked like a McKenzie consultant, no offense to the McKenzie folks in the room. I think something for me — it was actually started — when I started at Atlassian I thought I had to look older, because I looked five. I said, “They’ve got to take me seriously, I’m like 26 and have three months of HR experience, who is drunk and gave me a job offer?” I like “corporated up,” is the only way I can describe it. It was like a brown bob and like sleeveless silk blouses and no leopard print heels. That was because I thought I had to be a thing.

Aubrey Blanche: Something I did over the last year was actually started very slowly incorporating the way that I would dress myself outside of work. I ended up with weird hair and visible tattoos and leopard print. It was like I started wearing bright colors and then I was like, “It’s fine, I’ll just dye my hair all pink.” I’m the head weirdo, it’s my job. Then I got the very visible tattoo that I’ve been thinking about and that was something that was really important for me. I found that I became more authentic in my behavior to people because I allowed little pieces of myself in first. That’s what I would offer if it’s something where you’re not sure you’re not confident about it yet, is to give yourself space to do little bits at a time so that you feel safe and comfortable to show up.

Aubrey Blanche: Like I said, you don’t have to bring your whole self. You get to keep whatever you’d like to private or out of the workplace. My hope for everyone is that you bring in what you would find meaningful and important to bring in.

Audience Member: Hi, thank you for all your talk, I really enjoyed it. More than a question, maybe I’m looking of a little women’s support here. I’m looking to change my job so I’ve been out applying and giving interviews. I’m a very optimistic positive person, so I don’t want to sound negative. I’ve been to at least five, six onsites. I’m feeling a little bit of a high expectations because I’m a woman. I never realized this, but maybe because I’m in the more higher range in the age.

Audience Member: When I go for interview it’s only the 20s and the 30s and I love you all because I was there. They’re interviewing me and I don’t know what they’re looking for, because I feel I’m doing really well. Then they come up with some reasoning which doesn’t make sense to me. I know I’m not perfect, I’m an average hardworking person. I’m not a genius, but I’m at a place where I want to look for a change because I want to. I changed. I come from a history background and it was in my 30s I decided to get into IT. Then everybody told me, “You can’t do it,” and I got a couple of some certifications, I did Java. Now I’m a front end developer, but I feel that I don’t know what I’m missing. Am I missing the buzz words or am I not quick enough to do all those classes how they want me to do? I can still do it, because every job if you ask any of my manager they’ll be like, “Wow. You can do the work.” It’s a question or advice or whatever I’m looking for.

Audience Member: How do I go about it because I still have, I still get calls and I know I’ll go for more onsites. I just need some guidance I guess.

Ritika Nanda: Did you want to take …?

Audience Member: I just need to say [inaudible].

Ritika Nanda: Oh okay.

Aubrey Blanche: Let’s do it.

Audience Member: Hi, I work at Twitch, which is about as a young a company as you can imagine. I’m in my 50s. What I found when I went into my interview was I owned my age. They said, “What’s your favorite video game?” I said, “Well I’m actually older than that. My favorite computer game is Zork.” I expected the reaction might be, “Oh my God, she’s so old,” but what I got was, “That’s really cool.” The best suggestion I can have for you is just own your age and make that something about you that’s cool and fun and interesting rather than something to be worried about.

Ritika Nanda: I don’t think I would have been able to give a better answer, that’s for sure. Yeah, I definitely think that one thing, like this is Silicon Valley, so I think since you’re in IT and I’ve also been working in IT, you might have more experience and actually I could seek advice from you. Just in my experience what I’ve seen is that Silicon Valley, giving interviews, getting rejected, going for the next one is a pretty common trend. I think the most important part is not to let it get onto you and just keep trying until you find the one you like and they like you. It will just be fine, I guess. It’s just like a regular thing it’ll go on and it’ll be over. Like that’s what I think.

Aubrey Blanche: I was going to ask the panel a somewhat interesting question that is related to this, which is, tell me something that would go on your resume of failures in job acquisition. I will start and give you an example. When I tried to get into tech I applied for 127 jobs and got three call backs. 127 jobs and got three call backs. Thinking about five or six onsites, that’s actually a pretty good hit rate.

Ashley Faus: I graduated into the 2008 recession, it was terrible. Then I also moved out here. I’m in marketing, which is really hard to prove that you know what the heck you’re talking about till you’ve been in there for now a decade. I worked at Starbucks and the CEO of the failed startup that I was at actually came in to my Starbucks. It was pretty much the most embarrassing moment of my life to have to like serve coffee to this person. To be fair he failed at a startup, but like as a 23 year old I was like, “This is my thing.”

Aubrey Blanche: Any other resume failures?

Lori Kaplan: How long do we have?

Aubrey Blanche: Lori, pick your favorite.

Lori Kaplan: I know. I have had many. I’ve been laid off four times, which is common in Silicon Valley, but it feels disruptive and diminishing each time. Each time I’ve landed way better and into a role where I was welcomed and learned more and had a better opportunity. I say keep at it and ageism is real, but let your star shine out. That’s the thing. Like if you show up as yourself and you know — who was it that said, know what you’re good at and don’t be in a box? Show up with your expertise and your experience and let it shine and you will find the right fit.

Aubrey Blanche: Atlassian.com/careers. I’m contractually obligated.

Shauna: Can I jump in from a recruiter perspective?

Aubrey Blanche: Yes, we have a recruiter here.

Shauna: Hi, I’m Shauna. I work here. It’s pretty cool. Potentially controversial opinion, ask us what the onsite’s going to be about. Recruiters know who’s interviewing you, what they’re supposed to be talking about. If they’re good at their jobs, they know what questions they’re going to ask you. If we have the time, we do what we can to prep the candidates. We don’t always have the time, we’ve got 15 roles and 30 candidates for each role. If the candidate asks me to prep them for their onsite, I’m floored. I tell the hiring manager that that’s happening, that they’ve asked for it, and they’ve asked for the extra time. I’ll take the time if I can. Doesn’t mean that everybody can, but if you ask for it, you’ll walk into that onsite way better prepared than you would otherwise.

Audience Member: I have a comment and a question. Thank you.

Aubrey Blanche: Sorry, I don’t do rules.

Audience Member: That’s okay. My comment for the lady who’s interviewing is… I have an age problem. I’m on the older side, I’ve been in tech for…

Aubrey Blanche: It’s an age advantage.

Audience Member: Advantage yes, see I guess it is an advantage because I’ve been in tech for over 20 years. Yeah. I would have to say that I’m working with some folks out of college and I’ve learned so much from them about job hunting, so talk the people that are just coming in. They have different tactics than when I went got a job when I was in my 20s, one of the guys I work with, he contacts people over LinkedIn. He contact and call and gets on phone calls with them. My question is to you guys is and the recruiter, is that normal? I was flabbergasted when I heard that, but is that acceptable? Do people get on the calls and have info sessions before they interview? Thank you.

Ritika Nanda: I would like to share like I have had so many people who are especially fresh grads, they’ve contacted me through LinkedIn. As she mentioned I don’t always have the time, but if I have the time I’d be more than happy to help anybody and guide them. If I’ve been lucky I’d be more than happy to share my knowledge and steps. The struggles which I went through — if I can help somebody with that I’d be more than happy. I’m pretty sure a lot of people would think that way and if somebody you contact through LinkedIn — I have also tried to contact a few people and get some mentorship. I think there are people out there who do help you and LinkedIn is a good source. This is really true and does happen, so to answer that question.

Aubrey Blanche: Yeah, the advice I would give is do not ask someone to pick their brain. That’s just so nonspecific because it’s like, if you’re busy, this people do this to me a lot. If someone was like, “Here is my question,” it’s like even if I can’t make the time to meet with you, I might be able to like type out an answer and help you. Asking someone for a specific thing they can help you with increases the likelihood that you’ll get something helpful out of it.

Ritika Nanda: Right, sorry. One last thing which I wanted to add was, that when somebody contacts me, if somebody puts in a little extra effort and say, “Hey I’ve tried this, this is not working. This is what I’m interested in,” that then that genuinely shows me that he or she is really interested and I can spare a minute of my life to help him or her. Most cases I do that, so I think a lot of people will do that. Yeah.

Lori Kaplan: Yes to do it and sometimes it’s the mom network that puts them up to it. Having young adult kids and trying to help them land a lot of times, I’ll just talk to my friends and say, “My child is interested — my young adult — is interested in thus and such. Would you be willing to talk to them about this and that?” Then I make them make the contact. Just in the last two weeks I’ve had coffee with two different people that I peripherally know who are interested in Atlassian. They said, “Oh, you’re at Atlassian. Would you help me prepare for my interview or can you tell me what it’s really like to be there about this and that thing?”

Aubrey Blanche: Dominique. What about you?

Dominique Ward: In my past life I have had many new grads or “I need an internship” reach out to me for roles that I have no like connection with. A really just kind of like trying to get in the door and have a connection because they know that you’re more likely to get an interview or get your foot in the door and get your resume seen if you are a referral. Young kids are very savvy.

Aubrey Blanche: That’s true. We’ll do one more question because I want to be mindful of everybody’s time. Yes.

Claudia: Awesome, hi everyone I’m Claudia. I have a question for Aubrey. When you think of building balanced teams and this whole idea, a lot of conversations I’ve had with people in this field, they always say, “It starts from the top.” I work for a company that is very white male at the top. I was just curious if you agree with that it starts with them and if so how do you go about getting them on board with all that you’re doing.

Aubrey Blanche: At first I was like, oh you work at a tech company? It’s true right, who gets funded 20 years ago is who’s running companies now. I think it does start at the top, but I also think, what I’m seeing candidates do a lot right now is they simply just look at the representation of the executive team. Then decide whether the company cares about D&I. I think that’s actually not a complete heuristic. I don’t think you should use that as the only signal, because if you’ve ever tried to build an engineering center in Sydney Australia, well just try that. Then try to have a balanced executive team, because of the historical legacy of that. I think it’s so useful here as what’s more important is actually asking what the executives are doing to help build balance.

Aubrey Blanche: I don’t think he’ll get mad at me for sharing this. For example, about a year ago I [inaudible] — we have two CEOs and they’re Australian. I basically flagged to him, I was like, “Hey.” We’ve had a lot of conversations about him being a straight, white guy. I was like, “Hey, our black employees, they’re not having the same experience as other people. It’s showing up on our data, Latinx folks, we’re all happy.” I said, “One of the things that’s coming out in the comments is that they don’t feel like leadership is advocating for them.” We had a really frank conversation and he was like, “Oh, fuck.” He was like, “No, but I do care, I just don’t know what to say.” I was like, “All right, oh why don’t we give them voice.” He literally sat down for an hour and every one of our black employees was invited to call in and just talk to him. He was awkward and he got rules about what he was allowed to ask versus not. He was perfect. He walked, he’s like, “I have heard that you don’t feel like I’m advocating for you and I’m nervous and I don’t know how, but I’m trying to understand and hopefully you’ll help me figure it out. Please believe I’m trying to do well and tell me when I’m not.”

Aubrey Blanche: We had a really raw, uncomfortable, honest conversation and now — so that happened last year. Then he and I were preparing to give a keynote in Europe. I promise this has a point. In Europe, like the word race is very different than here. If you use the term race in English it’s like very Nazi-adjacent. A lot of our European kind of started feeling uncomfortable saying the word race for that reason. I never say things like people of color when I’m giving a talk in Europe. This is for our user summit. I’m in like a private rehearsal with some VPs and Mike, he’s like, and I listed a bunch of under represented groups. He’s like, “Why don’t you say people of color?” I was like, “Well because it’s Europe and the market doesn’t get it.” He’s like, “Yeah, but our employees are going to hear it, you have to put it back in.” I think that that’s the stuff that’s important, because the fact is our CEOs are straight, white men who are billionaires, but they back me up in the room when I’m not there. Or they are like, Mike is like, “No, our black employees are going to hear that we didn’t say that and that’s not acceptable.”

Aubrey Blanche: I think that those are the things you should ask about and that’s what you should do as a candidate. Right, don’t ask like, “Does your company not care about diversity?” Yes, of course we do. Don’t be like, “What programs do you have?” Your average hiring manager won’t know. What you can ask is something like, “What have you done to help people have more of a voice? How do you try to include people?” Like that’s something that anyone should be able to answer, so that’s what I’d say. I also don’t think starting at the top is enough.

Aubrey Blanche: At Atlassian, the reason we’ve been successful is because our leadership gets it. I do not justify my job here. We talk about how, but also because our culture and grassroots support it. There’s so many things that have been built in this office that were just built by Atlassians. I had nothing to do with it and that’s the mark of success is when I’m useless. I think it’s that, it has to be top down, it has to be bottom up. I wish I had a more helpful answer, but that’s it. Yeah.

Dominique Ward: Can I say one thing?

Aubrey Blanche: Please.

Dominique Ward: I just started two months ago and Aubrey did my onboarding. Before we even started she gathered the new people and we were going upstairs. Before we even went in the direction of the stairs she said, “Are stairs okay for everyone?”

Aubrey Blanche: Did I do that?

Dominique Ward: Yes.

Aubrey Blanche: I mean sounds like a thing I would do, but I’m thrilled I did that.

Dominique Ward: As someone who like disability rights and accessibility’s really important to me, that was a very subtle thing that she could have done that just like, I was like, “Okay, I’m in the right space.” Even though I accepted the job and heard about the values, I’m like, “Is that really what it is?”

Aubrey Blanche: I’m like a little teary right now. Anyone on my team will tell you I cry all the time. Oh that makes me so happy. I feel like I want to end on that note, but it’s a little weird and self serving. What the note that we probably should end on is an enormous thank you for the absolutely fantastic brilliant panel that you have in front of you. Also claps for all of you who showed up tonight and for all the things that you’re doing because the fact is that you’re really the future of tech and I’m so grateful that you’re all here.

Thanks to everyone for coming out to Atlassian Girl Geek Dinner!

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