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Some of the most important decisions in your professional career will be made for you… when you aren’t in the room.
During an inspiring keynote at Girl Geek X’s ELEVATE 2024 Virtual Conference, Amazon Head of Product, Research & Science Corliss Collier shares her blueprint for crafting a strong personal brand that opens doors and helps women in tech stand out amongst their peers.
Collier reveals how she used intentional personal branding to establish herself as a connector and rise through the ranks at Amazon. She outlines how anyone can become the go-to person by consistently delivering on your brand promise.
She recommends developing your personal brand through a continuous process of self-discovery, reflection and feedback.
Identify Your Unique Strengths, Skills and Passions
To craft a compelling personal brand, start by looking inward. Reflect on your unique combination of strengths, skills and passions:
• What are you exceptionally good at?
• Which skills do you possess that are valuable and in-demand?
• What lights you up and energizes you?
Think about the projects and roles where you’ve made the biggest impact and felt the most in your element. Identify the common threads – therein lie your superpowers.
Ask yourself: What do I want to be known for? What makes me stand out from others in my field?
Craft a unique value proposition (UVP) that encapsulates the essence of what you bring to the table. Your UVP should:
• Highlight your greatest strengths
• Align with your passions
• Speak to the needs of your target audience
• Differentiate you from others
For example: “Product leader who combines deep technical expertise with a talent for translating customer insights into innovative solutions that drive business growth.”
By getting clear on your unique value and the problems you’re exceptionally equipped to solve, you lay the foundation for a powerful personal brand that creates new career opportunities.
Reflect on Your Values and Goals
As you craft your personal brand, it’s crucial that it aligns with your core values and the direction you want to take your career. Your brandshould feel authentic to who you are at your core.
Ask yourself:
• What principles and beliefs guide my decisions and actions?
• What impact do I want to make through my work?
• What types of projects and roles energize and fulfill me?
• What kind of leader do I aspire to be?
• What legacy do I want to leave?
Use the answers to these questions as a filter for your personal brand. Every element, from your unique value proposition to your leadership style, should ring true to your values and aspirations.
For example, if innovation is a core value, your brand should reflect your ability to think outside the box and drive positive change. If empowering others is important to you, your brand should highlight your talent for developing and inspiring teams.
By ensuring your brand is rooted in your authentic self, you’ll project a consistent, credible image – one that attracts the right opportunities and enables you to make your desired impact.
Understand Your Target Audience
To effectively reach the right people with your personal brand, you need to get clear on your target audience:
• Who are the key decision makers and influencers in your industry or target companies?
• Whose attention do you need to capture to open up exciting opportunities?
• What do they care about? What challenges are they facing?
Once you’ve identified your target audience, tailor your brand messaging and style to resonate with them:
• Highlight the aspects of your unique value proposition that speak directly to their needs and priorities
• Adapt your communication style to match their preferences (e.g. high-level vs. in the weeds, bold vs. understated)
• Show up and engage in the spaces where they spend time, whether online or in-person
For example, if you’re targeting startup founders, your brand should emphasize your ability to thrive in a fast-paced, ambiguous environment and drive results with limited resources. You’ll want to have a strong presence on platforms like Twitter or Hacker News.
If you’re aiming for an executive role at an enterprise company, your brand should exude steady leadership, strategic thinking and a talent for navigating complexity. Publish thought leadership on LinkedIn and show up at high-profile industry events.
By understanding your audience and tailoring your brand to resonate with them, you’ll be able to get on their radar, earn their trust and inspire them to think of you when game-changing opportunities arise.
Seek Feedback to Refine Your Brand
To ensure your personal brand is hitting the mark, regularly seek out candid feedback from a trusted group of advisors, including:
Mentors who can offer sage guidance based on their experience and industry knowledge
Sponsors who are invested in your success and can provide insight into how you’re perceived by key decision makers
Direct reports who can give you valuable input on your leadership and communication style
Ask them questions like:
• What do you see as my greatest strengths and unique value?
• What words would you use to describe me and my leadership style?
• How am I perceived by others in the organization?
• What could I do to enhance my brand and increase my impact?
Listen carefully to their feedback and reflect on what you hear:
• Does their perception of you match your desired brand?
• Are there any disconnects or areas where you need to course correct?
• What insights can you glean to further refine your brand?
Use their valuable input to identify opportunities to adjust your approach and amplify your brand. Treat it as an iterative process – the goal is progress, not perfection.
By proactively seeking feedback and adapting accordingly, you can ensure your brand continues to resonate with your target audience and opens doors to exciting career growth.
• Consistently deliver on your brand promise. Your brand is a commitment to delivering a certain experience and results. Build trust and credibility by consistently showing up in alignment with your brand.
• Maintain a consistent presence. Ensure your online and offline presence, from your LinkedIn profile to your leadership style, consistently reflect your brand. Authenticity and alignment are key.
By being intentional and proactive in crafting your personal brand, you can shape how you are perceived and open doors to exciting career opportunities – even when you’re not in the room.
Amazon Executive Offers Critical Career Advice to Women in Tech
In her closing remarks, Collier offered some powerful advice for women in tech looking to accelerate their careers:
“Don’t wait for permission or perfect timing to take control of your career narrative. Start being intentional about your personal brand today. Reflect on your unique value, gather feedback from trusted advisors, and put yourself out there. Your brand is your reputation – craft it wisely and nurture it continuously.”
She also emphasized the importance of building a strong network of sponsors and allies who will advocate for you behind closed doors:
“Surround yourself with people who believe in your potential and are invested in your success. Cultivate genuine relationships built on trust and reciprocity. When opportunities arise, you want to have champions in the room who will vouch for your abilities and fight for you to have a seat at the table.”
By being proactive in shaping your personal brand and building a supportive network, you can open doors to exciting opportunities and make a lasting impact – in tech and beyond.
June, 5, 2024 – Girl Geek X: ELEVATE Conference and Career Fair for mid-to-senior women in tech hosted over a thousand women & allies globally, with 85% attendees interested in hearing about jobs, over two dozen speakers, & recruiting at virtual Employer Booths. Help a girl geek land her next job in tech!
Katie Ledoux (Chief Information Security Officer at Attentive), Neha Srivastava (Staff Software Engineer at Attentive) and Margho Dunnahoo-Kirsch (Director of Recruiting at Attentive) speak about the company, hiring, open roles, and more.
Attentive® is the AI marketing platform for leading brands, designed to optimize message performance through 1:1 SMS and email interactions.
Hi everyone. I’m going to introduce myself first. I’m Katie Ledoux. I am the Chief Information Security Officer at Attentive. Neha. You want to go next?
Neha Srivastava:
Yes. Hi, I’m Nehes Srivastava. I’m a Staff Engineer in the Product Engineering org at Attentive.
Margho Dunnahoo-Kirsch:
And I’m Margo. I’m director of recruiting over here at Attentive. Cool. Katie, kick us off.
Katie Ledoux:
Yes. Little bit about who we are, attentive, really pioneered text message marketing, and let’s give you a little bit of the Attentive experience.
I’d love to invite you to text “Hire me” to the number 2 1 7 1 8 and Neha, you’re going to put that in the comments, right? Thank you. You should be getting a message back from us shortly.
You may have interacted with some of our customers on SMS before, maybe you get texted a coupon code from your favorite brands.
We work with companies like H&M, Wayfair, Reebok, Margot just watched me through the companies I was allowed to name yesterday, Urban Outfitters, I’m allowed to name them.
Margho Dunnahoo-Kirsch:
Chanel.
Katie Ledoux:
Yes, and other funny ones that I can’t name, but just trust me, they’re funny in terms of where we are going as a company.
We’re really moving away from a world slowly, but over time we think marketing is going to move away from a world where our customers are sending massive messages to all of their subscribers or to big chunks of their subscribers and really moving towards one-to-one messaging.
I say slowly some of a lot of our customers are already doing this may be some more legacy customers, it’ll take them a little longer to move in that direction.
We really want to go to a place where we’re sending you as a subscriber exactly the right message from exactly the right brand that you’re interested in, at exactly the time that you want to make this purchase for an item that you are actually interested in buying instead of a link to a page of a billion different things that are on sale.
That’s the journey, and of course that’s powered by AI and ML and Neha. Maybe you can…
Neha Srivastava:
Yeah, absolutely. A little bit more. Yeah, absolutely. As Katie mentioned, Attentive Engineering’s biggest challenge right now is to deliver extremely personalized experiences to subscribers of our clients.
Now this isn’t very interesting problem if you really think about it because unlike others, we’re evolving from being a marketing tool to an active partner to the marketing departments of various companies.
We are doing lots of exciting things, which range from writing AI ML models to generating product recommendations and figuring out the best time to send a marketing text. And this is all driven out of the personalization. This is using AI and ML for the benefit of marketing to drive higher productivity, but also great experience for the end user.
Instead of getting a generic text that says, “Hey, Neha, come buy shoes,” and all the links that you get are all men’s shoes, which don’t even appear in my size, which leads to a frustrating experience, you would actually be directed to, “Hey Neha, you were checking out this great shoe at your favorite brand the other day. We found some other recommendations that you might like, which are in my size and available” and I can buy right now and potentially even in my budget.
Driving this life of type of personalization is a very complex engineering challenge and I’m very excited to be working on this. By the way, that’s my project, so that’s why I can talk too much about it.
The problems we’re looking to solve are ahead of the game, yet complex and challenging so that if you’re like me, someone who gets excited about solving complex problems for the business, you’d absolutely love it here.
Generally speaking, our product engineering orgs sits in a New York platform, is remote and AI ML teams sit in SF.
However, almost all of the projects are extremely cross-functional, so regardless of which location you’re in, you’ll end up working on the same projects and you’ll get a piece of the pie and problems that you would love to solve.
We’re hiring across the board and Margo will tell you all about that.
Margho Dunnahoo-Kirsch:
Awesome. Thanks Neha. Alright, let’s talk recruiting. Our engineering org is about 230 people spread out across the US.
We do have offices in San Francisco and in New York. Our interview process for a standard software engineer is really consistent across all of our teams and locations.
It’s about a 3-week process starting off with a recruiter screen. Then, you move to a 60 minute interview with one of our senior engineers. That conversation is going to be really digging into past experience. What was your role? What was the complexity of the work?
You’ll do a backend coding challenge and then that will be followed up by a reverse architecture conversation.
Once that is complete, we invite you to meet with about four to five more members of the team. This includes coding challenges and architecture interviews, and then discussions with hiring managers.
Don’t worry, a recruiter will prep you for all of that beforehand, then we also do do team assignments at the debrief stage.
We try to really match you with a team that aligns with your experience and interests, and then we’ll get you set up with a few members of those teams so you can learn more about what your impact would be, what you’d be working on, all that kind of stuff.
We have two offices, so we have New York and San Francisco. New York is our headquarters, but the majority of the employees are remotely. I’m actually coming at you from Denver, so this is where I am, home base. My team is primarily in San Francisco, but I do feel super connected to everyone.
The company has a really good job of driving engagement, which brings me to our culture and talking about our employee engagement team, so we do a full company offsite, annually. We do engineering team offsites every year, but then we also do a lot of virtual engagement activities.
Our employee engagement team just hosted a few virtual events. My favorite was the how to make your spring, how to build your own spring roll. We had a floral arrangement class recently and then we also had a good one around understanding the anatomy of our anxiety to honor mental health awareness month.
Just a little bit about us. I know Katie Ledoux’s team is hiring, Neha’s team is hiring, so we would love to have you guys come stop by our booth and meet with us. And then a few members of our attentive Woman Engineering ERG group. Cool. We crushed it.
Katie Ledoux:
We did. Can we use our three minutes for questions?
Someone asked: If we hire entry level engineers, we do have an engineer two role up right now, but that’s the most junior role we have right now.
I can’t speak for every engineering team at Attentive, but it’s going to be important for us on some of these newer teams to make sure that we have the levels of leadership in place before we bring on brand new entry level people so that they have the mentorship that they need to be successful on those teams.
Margho Dunnahoo-Kirsch:
A hundred percent, yes. We just posted, we have three software engineering roles just posted.
One is on our BI team, led by one of our engineering managers, and then we have two engineering twos, one that just got posted for remote employees, and then one that just got posted for San Francisco.
Hybrid – You’d be coming on site three days a week to our San Francisco office in the financial district. And then, Neha has teams mainly looking for some senior level engineers.
Katie Ledoux:
I saw one question. You definitely do not need a background in AI machine learning to apply. If you go on the career site, there’s a breadth of roles across infrastructure security. it it’s definitely not exclusively AI ML roles especially.
Neha Srivastava:
I have no experience in AI. I’m not an AI expert. Just to be clear, I am leading AI ML project and I have no experience on it because the way we think about this is it’s a product, right? A model is developed and then we pipeline it all the way to make it into a product.
I am a backend focused product engineer, so my job is to make sure that the model is delivering value as a product. I’m overseeing the whole thing and helping with the design and the architecture of it, but I’m not doing the modeling myself.
Margho Dunnahoo-Kirsch:
Answering a few questions interested in non-engineering roles. Would you be able to hold a conversation with me? Yes, a hundred percent. We are hiring and go to market g and a, and I also oversee those teams, so a hundred percent can talk to you about that.
We are only hiring in the US for engineering roles. Yes, for a few of our sales positions, we do hire in London as well as Australia. But just for engineering for now, we are primarily just us. We do support visas.
In thisELEVATE session, Anran Li(Riot Games Engineering Manager), Jessica Burns(Boeing Data Scientist), andBrenda Garcia Lemus(YouTube Business Intelligence Analyst) answer questions about breaking into the field of data science, skills required for a business intelligence analyst role, and leveraging data in decision-making. They offer guidance on how to communicate effectively and tell a story with data, as well as what to do when the data contradicts what stakeholders want to hear.
Like what you see here? Our mission-aligned Girl Geek X partners are hiring!
Hey everyone, I can get started. We’ll do introductions first. My name is Anran. I’m currently an engineering manager at Riot Games. We make games like League of Legends, Valorant, Teamfight Tactics. Yeah. Jessica, do you want to introduce yourself?
Jessica Burns:
Awesome. I love the popcorn methodology. My name is Jessica Burns. I’m a data scientist at the Boeing Company with the Boeing Global Services Division for Total Quality. I’m part of a pioneering data science team. I’m a co-lead for our team and I’ve done everything from finance all the way to software engineering at Amazon. Data science at Boeing. Career transitioner, Hackbright Academy alum, summer of 2015. Go 11 Zs. I’m very glad to be speaking with you guys today and let’s go ahead and hear from Brenda.
Brenda Garcia Lemus:
Thanks, Jessica. Hi everyone. I’m Brenda Garcia Lemus. I’m currently a business intelligence analyst at YouTube. I work in the YouTube business org, so I do a lot of data analysis and provide insights and automated ways or create dashboards for our business stakeholders so that they can make better decisions moving forward. And that’s it for me.
Anran Li:
Cool. Yeah, I can talk a little bit about my background as well for folks who are interested. I started off my career at Microsoft. I worked on the Halo games in particular. I worked on a lot their backend systems like matchmaking, skill ranks, also your profile and customization, things like that. I end up using data to leverage a lot of that job because skill ranking or how good you are, that’s all based on data, where we think you are compared to everyone else. Then we’ll slot you into are you bronze, are you silver, are you gold? Things like that.
After that I worked at Twitch on mostly commerce products. How do we help creators make money on the platform? Things like subscriptions, emotes, we built some things like hype trains or launched it to iOS. And we make decisions based on a lot of user data there. Like, Hey, how much money would a creator kind of usually make? How much do we think they’ll need to be able to sustain themselves and have streaming be their full-time job? What kind of products are we going to launch? Do we want to sell emotes? Do we want to just encourage the community to subscribe more? Sometimes it’s qualitative. We talk to streamers directly. What would be the biggest aid for you? What’s your biggest problems right now? What are some of their product ideas? They’ll have subscription goals and or follower goals, things like that and how can we support them?
Currently at Riot, I can’t talk too much about what I do. I work on the unreleased team, but I can probably, if folks have questions about League of Legends or Valorant and how they might use data, I can try to extrapolate based on what I know.
Jessica Burns:
Awesome. A little bit about what I do. I basically work with my team, and again, I can’t say a whole lot about what we do specifically, but a lot of it has to do with visualization as well as model creation and deployment for different kinds of quality solutions that will help with the end-to-end quality tracking process and compliance for aerospace. That’s primarily what me and my team do.
Plus we also create what is known as the central tower of data, so we’re kind of like a mix of data science, ML ops, data engineering, analytics. We run the gamut so we’re not just one thing. I know that there are some teams at Boeing that focus specifically on one, but we kind of capped out a mix of a lot of different things. Like right now I’m actually even working on a web app that interfaces with many different data sources to augment what wasn’t originally created with the original package that we got from a third party vendor to basically make that a little bit more robust for our senior level management and to basically increase transparency throughout the data pipeline process, and that goes from vendors, suppliers, and us all the way to our end customers at various airlines as well as our customers in the federal government.
Brenda Garcia Lemus:
That’s awesome, Jessica. Anran, for sharing, I can talk a little bit more about my background and a little bit about my current role and my journey to get here. I transitioned to data related roles after working as a research analyst in the consulting field. During my time as a research analyst, I started to work with data, and this experience really crystallized my passion for data analytics.
My first pure database role started at a policy think tank and then I transitioned into data roles in the entertainment industry at Disney and now in tech at YouTube. I do think my education helped make the transition a little easier because I did econ but specialized in stats and econometrics, so that definitely helped.
I do think doing individual learning also helped. Learning SQL on my own was something that I had to pick up, and then also Python. I think that’s how I went to where I am today.
Jessica Burns:
That is awesome. Thank you so much. Brenda. Should we go ahead and get into some of the Q and A?
Anran Li:
Yeah, that sounds great.
Jessica Burns:
Awesome. Gianna, and I hope I’m saying your name properly, says I’ve been in tech and HR tech for almost 20 years and I want to get into data science. I started classes, but trying to figure out how I break into a new field this far into my field, I think she means career. Any suggestions? Who wants to start? Okay, I think I’ll go ahead and start then.
I’m a career transitioner as well. Like I said, I used to be in finance for a long time. I was a business and planning analyst. I was an estimating and pricing specialist. I was a senior estimator for a long time. I was in procurement financial analysis. And so I would say one of the things that really helped was in your own space where you are try to apply data science or at least data methodologies to whatever it is that you’re doing.
Basically, taking a more data focused approach to whatever it is that you do will position you to have transferable skills within your niche. Because I don’t consider myself just a data scientist. I do have an entire career behind me that is where I understand financials as well. I’ve been in high finance at Smith Barney that doesn’t just magically poof away with my transition into more of the engineering side, same with my entire decade plus of experience in accounting.
I mean, I am a data scientist plus, and so you would basically be data science plus HR, and that’s a very valuable thing to have is going deep into a niche is actually really where it’s at. Whatever you can do, start playing around with things like Qlik or start visualizing your stuff. I don’t know if everyone here is familiar with pandas, you can play around with it. You can do some really cool visualizations out of the box in Python. Starting to do that sort of thing first and then saying, okay, well I do have the track record. I have been working with things I know how to think in terms of strings, in terms of cleaning the data, in terms of thinking about edge cases, that sort of thing.
You can do what you’re doing right now, your own domain, but you can add this additional skill. In fact, that’s why I decided to do this was because I was tired of waiting for, I would write things that would break Excel, and so I was basically waiting for Microsoft to either come out with a new version or I needed new tools.
That’s why I decided to go to Hackbright and I came out of Hackbright learning Python, and I didn’t need to have the shackles of Excel or any other Microsoft product because I had different libraries that could accommodate those things. Then I could also augment my data with other data sources for additional insights that might be beyond the confines of my organization or my team. Brenda or Anran?
Brenda Garcia Lemus:
Yeah, I also transitioned into data science. I studied econ both in undergrad and grad school. I did specialize in stats, so that helped, but I definitely had to do a lot of on my own learning. It helps if you jump into different sites, there’s so many resources, including free ones to really supplement your skills, like SQL, Python, R, and also building a portfolio really helps, especially if you don’t have any experience in data analytics or data science, just so that you can showcase like, “Hey, I can actually do this stuff.”
Then, just being resilient because when I first wanted to break into data analytics, I got a lot of rejections to be honest, a lot of rejections, and you just need one open door and you just sneak your way in there and then just keep proving them that, yes, I can do this. As you gain more experience, it’ll be easier to transition into the industry that you want, but definitely, being flexible and open I think would be my recommendations.
Anran Li:
Yeah, definitely. Plus one to everything Jessica and Brenda said. I think on one side, trying to find data related things to do at your current role is a great idea. Same for Brenda of studying SQL or R or a lot of the technical tools that they’ll be using. One thing is, even though I’m in the engineering role and we have data analysts and scientists that support things I do. As part of my job, it’s really important to actually just go in there, look at the data, I’ll do SQL queries to find patterns and stuff. They’ll do presentations. It’s very important for me to understand what it means. One thing if you’re in HR specifically because I’m a hiring manager, I use tools like Greenhouse and they even have some data things on that backend. And one thing that I was interested in is how do we create a more diverse pipeline?
I went into some of their backend and I tried being like, what type of candidates do we usually get? How far do they make it through the pipeline? Then I created and ended up exporting some of that to Excel and coming up with a strategy and presenting it to some of the leaders in my org and some ways of running interviews to be like, Hey, look, it looks like if we just do first round screens instead of a phone interview, if we just have them do a test, we end up getting more diverse candidates, through the pipeline that way. And the quality we indicated the quality is not actually lower. Things like that.
You could try to find neat side projects in your role. Think about data as in every company uses it a little bit differently. I’m like, that’s a HR application. There’s some very deep AI machine learning type of applications that’s probably a little bit harder to get into. I helped Microsoft develop their true skill to algorithm or I helped them build it. I am like, I’m not smart enough with math and all that sort of stuff to help them create the algorithm. But that’s going to be a harder area to get into where you’re like, oh, ML is able to look at all things like how to kills or deaths or other actions that happen in Halo. You run a big query every nightly job and you change everyone’s–tunes everyone’s MMRs based on that, and it develops an algorithm for what they think are important heuristics that go into it. That is very advanced stuff. There’s also simpler things, like right now a lot of gaming companies, they play test a lot and every day.
And some of that is you’re bringing a bunch of play testers. You think about what type of questions like, is this game fun? How does the experience of going into this menu feel like? And a lot of that might be a little bit more qualitative data, but then that requires you to know a little bit more about your subject matter expert of what type of game is this, what makes this fun? Is it League of Legends?
If it’s more like the big moments or the outplays that really make it fun versus in Valorant might be more shooting base is the mechanic of shooting actually fun, is the macro strategy fun? I think data and if you think about it from that point of view, can be applied to a lot of different things. Also think about what you know and where you can bring value there.
Jessica Burns:
And just a quick follow up to that. It’s also helpful not to just think about it in terms of your job because I actually got my first titled data science job, even though I’ve been doing it for a while, while I was volunteering.
I was volunteering for a 501(c)(3), the Washington Technology Industry Association, and I was helping them with some of their advertising spin strategies as well as outreach to veterans. That was a volunteer position, so if there’s a cause or a charity that you think is worthwhile, consider doing some work with them to help them better optimize their limited resources as well as gain skills and get that valuable experience. You can do that as well.
Or even think about if you’re in school, you can do school projects or personal projects as well. It doesn’t necessarily have to be in your job. There are other opportunities for that as well.
Want to go to the next question that’s been asked. All right. We have Reolan, I’m sorry, Reolan asks, do you have any favorite projects that you have worked on, whether for your jobs or personal projects? Brenda, do you have a favorite one?
Brenda Garcia Lemus:
Yeah, so I think one of my favorite projects that I ever worked on was back when I was at Disney. I had to dive into data to give producers of shows a comprehensive view of how a specific TV show was doing, all with the goal, of course, of making it better. I thought it was really fun. It was like playing data detective to try to uncover what parts of the show were doing well, where we were retaining the audience better and then providing those insights to the producers. I thought it was really fun. It was a show I enjoyed watching, so doing the data work on it was pretty fun. What about you, Jessica or Anran, have favorite projects?
Anran Li:
Yeah, I can speak to it. So one of my favorite projects was I made the emote card at Twitch. There’s emotes in chat. If you click on it, a little card pops up, it tells you what the emote name is, what streamer it’s from, and all their other emotes, and you can go to their channel or subscribe. What came from that is we had this theory that folks might want to purchase emotes, but instead of just building a direct purchase, let’s do it in between stuff that’s a little bit easier, but it’s also helpful for folks to discover new streamers and things like that.
It’s cool. It came out of a hackathon project, it’s front end backend, all that sort of thing. What we ended up actually finding out is there is a subscribe button. You’re almost like, oh, if they like the emote, they’re subscribing and they can purchase the emotes. We learned that it did help discoverability for other channels. That’s great for the community, but folks did not really want to subscribe or pay for emotes except for AdmiralBahroo. He has those really cute panda emotes. His subscriptions went through the roof and then it barely affected anyone else’s, but he does have really, really good emotes.
Jessica Burns:
That is so cool. Oh my goodness. Wow. Actually, one of my favorite personal projects that I’ve worked on was actually a data and art combination, and I can go ahead and share you guys with you guys what I did. Let me go ahead and present share screen. Let’s see here. Here we are. This is actually a thing that I did during the pandemic, during the George Floyd protests that were going on. There were some songs that really spoke to me. And so I created this kind of this Cypher model.
Cypher is basically a product, or sorry, is a language, it’s a query language that is used with Neo4j, which is basically a graph database. And so I would take the songs of some things that I thought were really poignant and spoke to the moment, and then created a graph of the songs and the people who actually sang them.
I then was able to visualize how these different groups come together. I specifically found that there’s a really strong relationship between Run the Jewels, which is one of my favorite groups, and another one of my favorite groups Rage Against the Machine. And so I took that and I started working on, I kind of superimposing that on some images that I found that I thought were very poignant and spoke to the time as well.
I would go and also use Photoshop to create what were essentially image masks that I would then map the lyrics onto. And these are some of the final results was like this walking in the snow lyrics for, and these are actually word clouds. Basically we have to play with the interpolation, the way it lays out and everything like that. And I thought that it was a way for me to uniquely express my voice using the ethos of the moment and popular media to express how I was feeling about the conversation that the nation was having at the time.
This is a project that is very near and dear to my heart. It actually ended out a little bit better than I thought it would be. And I got to play around with working with language data, natural language and learning, taking a crash course in some stuff for related to Photoshop as well as Python tools to help automate this. This is some of my very favorite work that I’ve done just personally with data and storytelling from that regard, using data to just tell stories and to express yourself because it’s not just cut and dry. It can be many things.
Anran Li:
Yeah, that’s super cool. Jessica, I also really love that you were just passionate about it and just did it as a side projects.
Jessica Burns:
We have a question for Brenda. I have a question for Brenda. I’m looking for jobs in BI analyst role. Other than the skills you mentioned, what skills are required to get an entry into this role? What should one do to make the profile stand out more?
Brenda Garcia Lemus:
Yeah, I think that’s a really great question. I think BI analyst is an interesting role. You’re a little bit of everything. Sometimes in a way you have to create dashboards. As part of my role, I’m doing some of the data engineering pieces. I definitely think it’s good to have the core skills, for example, have very, very strong SQL skills. That definitely helps prepping for interviews and then doing a lot of practice problems just to get in the door. But
Another really valuable skill is also having some UX design background for creating helpful dashboards. I think that’s something that has definitely helped me succeed in a BI role is not just being able to have data dumps, but also being able to tell a good story through dashboards and make them user-friendly and also actionable. It helps to get familiar with the domain that you are trying to go to because it does help to have some business context.
For example, here at YouTube, it definitely helps to have background in how a little bit of media works and also how tech works and streaming and all of that. But if let’s say you’re going into healthcare as a business intelligence analyst, it definitely helps if you have some background in that as well.
It really depends on what area or what industry you’re trying to go into. And one way you could showcase this is maybe doing a personal project with publicly available data on that specific area that you’re trying to enter. For example, if you want to go into healthcare, maybe find some open source data sets and then putting together a dashboard, a data pipeline so that you can talk to recruiters and also during the interview process about this and how it would apply to your role.
That’s what I would recommend doing. And also, presentation skills are very valuable, so being able to communicate effectively and explaining your metrics, explaining the dashboard and how it can be used really helps.
Jessica Burns:
Storytelling is so important because a lot of places, data is new to them or they’re just trying to figure out how to leverage their data. So you’ll get a lot of requests for, hey, make me a dashboard, and then they’ll keep adding to it and adding to it and adding to it and adding to it. And at the very end, it’s basically just this big mess of data and it’s like, okay, well is this a call to action? What am I supposed to do with this?
Being able to help, having experience with not just how to get the data and bring it together, but how to craft it in a way that tells an actionable story that isn’t just like, okay, well here’s our sales from the last five years, but hey, maybe this one’s not a great seller. Let’s go something else. You need to be able to tell that story. Or, Hey, let’s stop doing this and start doing this.
That will basically put you a cut above the rest because a lot of people will just put a bunch of numbers up on the screen and be like, okay, we’re done. But there’s a lot of value there.
Anran Li:
The next question, if folks were in denial about a problem, have you leveraged logic or data over the hearts and minds of your teams and leaders, asked by Cassandra?
Jessica Burns:
Sometimes data can produce situations where you might have to express unpopular opinions. Data is political by its very nature. A lot of people will try to use data to either prove their point or disprove a point that they think is not correct. And if the data goes against that, then that can produce some very uncomfortable situations.
I know that when I was volunteering at the Washington Technology Institute, they have a technical assessment online that all the applicants take in order to see if they were going to get an apprenticeship at, say an Amazon or a Microsoft. I was like, okay, well, it looks like we have a pretty good bell shaped curve throughout the reading comprehension and the math portion. However, the soft skills, that’s where you’re saying is your competitive advantage where you have an edge over everyone. That is basically a single data point because most people know not to yell at somebody if they’re asking for a refund or something.
And that’s the kind of questions that people were having to answer. And I showed them on a chart compared to the other sections that was really not yielding any valuable statistically relevant differentiation. I said, you guys have to go back and raise that entire section, go back to the question bank and try to create something that is more rigorous, that is not nearly as intuitive and to basically that will answer that mail, but that will also yield results that actually are useful for your end goal.
Watching their eyes, the board of director’s eyes, while they saw that chart with just the sharp up down because most people knew exactly how to answer that was very, very valuable. And you also have to think about your audience. So you don’t want to embarrass your audience if it’s potentially going to be embarrassing for them or if they have a stake. You really want to also think about how you socialize it with people beforehand and so that it’s not like a bomb dropped on them where they’re just like, we don’t want to talk to her anymore because she’s politically dangerous or whatever. Yeah, there’s that.
Brenda Garcia Lemus:
Yeah, I totally agree that especially when you have to deliver not such good news with data, it can definitely be a very challenging experience. But I think it also really depends on the culture of your company, of your board, of how open they are to listening to data insights versus their own opinions and instincts. I definitely do think you have to keep that in mind, what kind of organization you’re working in, what kind of company you’re working in, and how they will take these answers. I do think that it’s still very important to present these findings, but I think what kind of helps soften the blow sometimes is to provide potential solutions. If you suggest this isn’t working, okay, if that’s not working, then what is working? That really helps to end things in a good note.
Anran Li:
Yeah, thanks. Yeah, while they were discussing their opinions, I was trying to think of a good maybe example for some of this. But yeah, I think in tech companies, we do a lot of AB testing. You launch multiple versions or multiple UIs of the same product. It’d be like, which one’s a little bit better? One kind of interesting thing I worked on was new players on Halo. We think we kind of set you at the average rating, but then there’s this hypothesis, yeah, I’m the first time playing a Halo game. I don’t know what I’m doing. Can I even move in the game? Who knows? I’m probably much worse than average. And then so we did a test where we kind of lowered folks’ average to see if they’ll have a better experience, and then we tracked retention slash engagement, how long you played and how often you played.
And then obviously we also tracked just kind of monetization metrics, how often do you purchase cosmetics and other such things in the game. But it’s interesting because I respected the culture there a lot that it did actually say that, Hey, I think new players have a better experience. They’ll engage for longer if you lower the threshold for new players. But actually the money metrics went down by a tiny bit, but the designer was actually like, Hey, I actually think it’s better for us to veer towards a better player experience because we also only ran this test over a month. He’s like, I think all that metrics will probably go up after that. It’s better for us to just like, if you like this game and you’ll play it for longer, you’re probably more likely to purchase things in the end, right. So yeah, that was really cool decision that was made.
Jessica Burns:
I suppose we can go to another question. How was…Miriam, I’m sorry, asks, how was the interview experience into getting into YouTube and other big tech companies? Do you do heavy, medium, advanced lead code prep. As a career transitioner, I find advanced algorithms of big bottleneck to getting into big tech companies. Why don’t we start with the person at YouTube?
Brenda Garcia Lemus:
Yeah, thanks Jessica. Yeah, I do think it is a commitment if you do want to apply for big tech, it is a big investment. I’m not going to lie in terms of prep, you will have to prep for months and the interview process themselves can take months. I have interviewed with Meta, with Amazon, with Google and all of them. I think Amazon’s probably the fastest one, but the other two can take months just to go through the interview process and you’re going to have to go through three, four, maybe even five rounds. So I do think it is an investment that you have to be willing to make, and it really depends on the role. If for example, you’re going for a BI role, I do think your SQL skills need to be advanced. Otherwise it’ll be very, very hard to get through the technical interview if you want to be a data scientist.
I would say also Python and R are absolutely crucial and they have to be at least medium capacity. So I think it is a commitment, but I do think if you practice, you get better. When I first interviewed with Amazon, I got rejected right away. And so it’s just having the ability to get up and go like, okay, I’m going to be sad for a day, but I’m just going to keep going and not let that stop me. So having…they say, practice makes perfect. So the more you practice, you can look at these interviews as another opportunity to practice.
Angie Chang:
Thank you for sharing your insights on data science, data engineering, and being an engineering manager in tech. This is really illuminating. I love hearing conversations about how to get started, how to find that next job, how to showcase your skills, how to learn more. Thank you so much for sharing these resources or in the chat, and we’ll be moving on to the next session now. Thank you.
Jessica Burns:
Just know that you belong here always.
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In this ELEVATEsession, Soumya Lakshmi(Director of Engineering at Adobe) speaks about developer experience (DevX): productivity, impact, and satisfaction as keys to quality and collaboration.
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Thank you Sukrutha. Hi everyone. Happy Women’s Day and thank you Sukrutha Angie and the ELEVATE team for giving me this opportunity on Women’s Day. I’m here today to talk about DevX. DevX is called developer experience. This is going to be a little geeky talk, so bear with me. It’s purely from the engineering side, but I promise I have a story to say, which is what I’m going to start with.
I grew up in India and reflecting on my childhood in India, we did a lot of train journeys. Train journey was sort of the internal part of our family outing. These adventures began long before the train even arrived. It sort of marked the anticipation and the flurry of preparation. And each journey meant packing our bags with care, ensuring that we have everything needed for the trip. Upon reaching the station, our next step would be to find a porter or a coolie, is what we call locally in the Indian language.
Now, watching these skilled porters effortlessly balance their entire family, our entire family luggage, where I’ll show you a picture, I hope it’s pretty clear. I try to get a picture where a porter is carrying a lot of luggages. There’s one couple on his head and there’s two, one on his right shoulder, one on his left shoulder, and then he just carries around. Now it’s a real skill to carry the entire family’s luggage on their heads and arms, and there was nothing short of remarkable. Now they carried our burdens, allowing us to navigate the crowded station with ease, transforming our potential strenuous part of our journey with this seamless experience.
Now, why am I saying this? What has this got to do with the developer experience? Now, this memory serves as a powerful metaphor for a challenge faced by our developers and the engineers today. In many ways, they are like the coolies or the porters of the digital world, just as the porter prepares the physical journey. Let me go to the next slide. There we go.
Just as the porter prepares for the physical journey by strategically balancing the load to carry our developers and engineering teams and engineers geared up to the journey of innovation, excited about the possibilities of deploying really exciting features, but they also weigh into the inefficiencies that accompanies with the role and these inefficiencies being slow build processes, inadequate infrastructure, sparse test automation, nebulous documentation, and ever looming shadow of the tech debt, which never gets over are the suitcases of the software development industry that exists today.
Now, these are necessary parts of the journey containing assets and tools along the road. Yet this is a cumbersome process, slowing the pace down, clouding the excitement, and at the end of it, it seems really tiring.
Why then should our digital porters or coolies, the developers and engineers whose innovation propels us forward, accept the struggle as given, just as an introduction of wheeled luggages, revolutionized travel for many of the load or managing the load because adding wheels to suitcase? It really did not change the functionality of the suitcase, but what it did is made a hard task easier, and that’s really what DevX is. That’s exactly what the crux of the developer experience is.
Let me talk a little bit about the recipe of what I think, and GitHub completely agrees with this, is of what a DevX is.
DevX can be viewed in many different lenses, and this has become a common buzzword in the industry, but a lot of companies have started to put as this is an org and this is a team and we are investing in it, but what exactly is this? And it can be viewed in many different lenses. I think that the formula for DevX incorporates few key eight things.
First, it takes into account how efficiently and productively a developer can do their best work on any given project. The second one, how simple is it to make a code change and how easy is it to move from idea to putting it into production? Today, if I have an idea in mind, how long does it take for me for that idea to be delivered in the hands of our users?
DevX also examines how positively or negatively the work environment, the workflows, the tools, the technologies that affect the engineering satisfaction. By eliminating some of these friction and inefficiencies, we can multiply our operational impact. Now, if we want to move fast, it is easy, but if we want to move fast with quality is when the tricky part comes.
Collaboration and quality is also the integral piece of what a DevX is. If our engineers are productive and if they love what they’re doing, and if collaboration is smooth and quality is the integral part of it, then we have a good DevX and DevX is great. Yes, we want everything. I mean, who doesn’t, right?
Let’s see. Okay, why is this important Now, why are we talking about this? This seems pretty obvious to some extent, but why is it becoming even more important now? Because of the macroeconomic climate in the industry, the economic uncertainty is shaking up the tech industry with increased pressure on infrastructure and engineering teams to optimize cost. At the same time, we also realized that the progress and innovation must be accelerated as it is the key lever to create business value and success for digital initiatives and boost revenue of organizations and with restricted budgets.
That’s the key point. There was a survey or a snapshot that was done February of 2023. It’s called the Forrester Opportunity Snapshot, and what they did is they looked across 500 enterprise companies across United States and they did a survey of what the companies think that they should be focusing on to innovate.
Now, this company who does this survey is their focus is digital transformation, and organizations are recognizing and making sure that the operational excellence is on par with a restricted budget. These were some of the results of the survey. I won’t go into a lot of details because it is a lot of numbers. I’ll still talk about the top four key findings that came out of the survey.
The first key finding is the need to increase efficiency as a key focus. Yes, there is no headcount. There’s no incremental headcount. The companies are not hiring as much as they were and the climate, the microeconomic climate is extremely challenging, but we still need to innovate. To keep up with the pace of the digital transformation, organizations are recognizing that the need for developers to build, deliver software with greater efficiencies before.
Me as an engineer, it’s been a while I wrote code, but as an engineer, if I’m able to write one pull request in one day, then how is my company, how is my company providing the tools and technologies for me to merge two or three per request? That’s where the industry is going, and that’s where the crux of DevX is. Now, according to this research, 87% of the leaders agree that increasing the developer productivity is a priority for the next 12 months, while 85% say that better meeting customer demand will be their focus, and 85% say that shortening the release cycles, but would be the key factors involved.
The second key finding is several obstacles will hinder developer productivity. Now, developer productivity is not as simple as, Hey, you give me a tool and a framework and I can make things happen. There are a lot of different things that go into the combination of uncertain economic outlook, increased competition, shifting, customer demand, and the hybrid work as well as the DevOps methodology. This is all highlighted in the report. If you take a look at these numbers, 41% of the respondents say that developer productivity and experience building difficult to improve because of pandemic related issues like onboarding, training, mentoring. The face value is gone, and I’m sure things are improving eventually, but we need to strike a balance and focus more on not just the user experience but also the engineering or developer experience. The key finding three is having an internal developer platform, or an IDP, to boost developer productivity.
What’s the solution? You just give an IDP and then that’s the solution. Well, according to the snapshot or according to the survey, they said that IDP enabled a self-service for developers, helping them to become less reliant on operations and reducing bottlenecks that caused by ticket ops and whatnot.
This is one of the biggest pain points caused by increasing complexity of cloud architecture. Not only do platforms help alleviate this challenge, but they also have a potential huge impact on developer velocity and satisfaction by optimizing developer workloads and freeing up teams to focus on value adding work.
And the last one is the developer experience impacts overall business. It’s not just that we make strides and we make improvements to the developer experience and only engineering teams is benefited. Let me go forward a little bit. There we go. This talks about the survey also took into account teams who already invested in an org like DevX, and this is what they found. They found that it not just improved the engineering productivity, but it improved app development, time to market, customer attraction and retention. On the delivery side, there was brand recognition reputation, and on the operations side we had revenue growth and developer recruitment, retention and profitability.
Alright, so I think there was a lot of numbers. What is the crux of this conversation and where are we headed? In conclusion, what I do, I have about five minutes and I can take questions after this. In conclusion, what I would really like to add is think about it like adding wheels to your suitcase. 20, 30 years we all traveled, lugging our baggage or somebody else carried it for us instead.
The simple solution of adding wheels really made all our jobs easier. We could just go anywhere in the world lugging our luggage right behind us because the wheels take care of it. The wheels don’t necessarily improve the functionality of the suitcase, but it does do a lot of heavy lifting.
Think of DevX as the heavy lifting of the software development, a thing of the past. And we are not just enhancing the developer experience, but we are also enhancing the growth and innovation in the coming years. Thank you.
Sukrutha Bhadouria:
There are some questions here. There’s one question. How do I get started with using DevX for my company?
Soumya Lakshmi:
That’s a great question. Depending on which stage your company is and at what point there is readiness, there might be a few different things. I can speak from Adobe’s perspective. Adobe, I don’t think a year ago DevX was even a thing we started talking about, like I said in the presentation, we were not there.
We were not hiring and headcount was crucial, but we still had to make improvements. But there were different teams and members of the team who were already doing this kind of work.
One of the things you could do is create a working group across different products within the organization to see what needs to happen and how you can share and reproduce to share and sort of reuse some of the frameworks and toolings that you’re doing. That could be the first step.
Then, meeting often online of course, I mean, and setting up a roadmap of what is important and what are the gaps, and at least starting this conversation in the devs direction might be the first step towards it.
I’ll also add that there are a lot of resources available online because again, all the companies, many companies are realizing that our user experience and customer experience is crucial, but so is our engineering and developer experience. That might be a good starting point.
I’m available and you’re welcome to reach out to me personally and I’m happy to provide guidance on that front as well.
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In this ELEVATEsession, Poornima Muthukumar (Senior Technical Product Manager at Microsoft) shares how data can help product managers validate their assumptions, test their hypotheses, and measure their outcomes.
Attendees learn to build data-driven products backed by insightful analysis and how to utilize big data, data science and machine learning to inform complex product decisions.
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Hi everyone. Good morning. Thank you so much for joining today’s presentation. I’m super excited to speak to all of you today on how to unlock product growth with big data, data science, and machine learning. Some of you might be interested in getting into a career either as a data scientist, business analyst, data engineer, technical product manager. so if you’re in any of these careers, I hope that this talk resonates with you and I hope that you can take back something for your job.
I also want to thank the Girl Geek IO for giving me this opportunity to speak to all of you today. And I want to add that I’m not speaking on behalf of Microsoft, but rather sharing the knowledge and experience that I have gained along the way in my journey. So yeah, without further ado, let’s get started. Brief look into today’s agenda so you know what you can expect from this talk.
First, we’ll go over my background so there is context on some of the things that I shared. Next, I will talk about how data is at the center of nearly every product you own and how that data is used to customize product to your needs, allowing companies like Netflix and Uber to build great data-driven products.
Next, we’ll talk about why companies need individuals who can use data from all of that big data, and what are those different data types that you as a product manager can leverage to extract insight to give customers the product that you want. And finally, if we have time, we will take some question and answers. Cool.
A brief background. I grew up in India. I spent a majority of my childhood in Mumbai and Chennai finishing my education in India. Post that I went to Singapore where I got my bachelor’s degree in computer engineering from the National University of Singapore. During my time at Singapore, I also interned at Bank of America and Goldman Sachs as a software engineer. After that, I went to New York where I worked in Goldman Sachs as a software engineer, building software for banking systems and capital market. After that, I went to Ireland where I worked in Microsoft Ireland research center as a software engineer in the office team. During there, I also traveled all across Europe, so that was a lot of fun.
After that, I came to Seattle where I grew in my career as a senior software engineer in the office release and delivery experience team at Microsoft. My team was basically in charge of delivering office updates that you got each month for all of your apps, like Word, Excel, PowerPoint on all platforms like Mac, iOS, windows, and Android. During this time is where I realized the power of big data and decided to pursue my part-time masters in data science from the University of Washington.
I also transitioned into my career as a senior technical product manager for the Microsoft 365 team because I wanted to have an end-to-end breadth of ownership of a product and be able to do that in a data-driven fashion. Today I am a data science volunteer at the Women in Data Science Puget Sound Community. I own patents in AI,ML, and big data at Microsoft. I am also volunteering at the UDub Foster School of Business as a product management accelerator.
Here I have five products that I want to quickly talk about how these companies are using data to drive their product growth. Netflix is something that all of us know how. Netflix uses data to build a recommendation model. They also use data to decide how to invest their money and what kind of producing content that resonates with user. They also use data to decide which movie to store and which CDN location based on where the users are streaming movie from in order to efficiently stream movies so that they can optimize for storage cost of CDN.
We know Tesla uses data for powering their autonomous driving system. They also have these cameras and sensors that’s constantly sending data back to Tesla, which in turn is used to optimize their self-driving car.
Amazon is one such product that uses data throughout their entire product stack. They use it for their search result optimization for price forecasting, warehouse optimization, inventory management. There’s just many, many ways that Amazon uses data because it has such a huge customer base. They have all of that huge amount of data which they can use to build and improve their product constantly.
Instagram, I’m sure all of you are aware that all the reels and all the contents and all the things that you see, there is a machine learning model that is running real time customized for you.
That is taking in all your engagement data, that is taking in all your usage data, which in turn is used to customize the model and send data back to you, which in turn gives you content that resonates with you in order to keep you on the product longer.
Next, we have Microsoft 365. Obviously now we have copilot. We have all of that ChatGPT integration that integrates with all your different Office 365 apps in order to give you in order to optimize your productivity suite experience with Microsoft, so if you see what is common to all of these products is they have a huge customer base that generate a huge amount of data, and today’s storage and compute and processing has become so cheap that you can store all of this data.
You can run data science techniques, you can run machine learning models, you can run algorithms on top of it to extract in site, which in turn can be used to optimize your product, which in turn can be used to build products that delight your customers.
Let’s say you join as a product manager for any of these products. You are constantly getting data from various signals. Could be feedback data, could be usage data, could be finance data, could be sales data engagement, data retention data.
How do you as a product manager organize all of this data in a clever way, in an intelligent way so that you can extract insight, which in turn can be used to drive product growth? How do you leverage those different data science algorithms techniques to optimize your product? Which is why I feel that the future of technical product management involves the melding of data science and product management because there’s so much that you can leverage to drive product optimization.
What you can expect from this talk is how to build data-driven products backed by insightful analysis and how can you utilize big data, data science and machine learning to inform complex product decisions.
Here are list seven techniques that I use in my day-to-day job to drive product growth and use data to drive them. First, I list the seven techniques, but because of the time constraint, I’ll only go in detail into three of them today in the talk. Tthe first one being funnel analysis, funnel analysis, how do you look at your customer journey end to end and see where customers are dropping off in the funnel so you can optimize your customer journey and thereby improve the conversion rate.
Next is retention analysis, right? Retention is a very important metric for any product. It’s great to have customers sign up for your product, but you also want to see of that, how many of them are actually using your product? How many of them are enjoying using your product? Let’s say you have a subscription service. You want to know what percentage of customers are renewing your subscription versus what percentage are canceling your subscription.
Next is segmentation analysis is how do you slice and dice your customers segment based on different things? Could be customer demographics, could be age, income, gender, their preferences, their needs of their purchase characteristics. How do you take all of this different data and slice and dice your customer into different segments, which will help you identify your most profitable segment and in turn cater your products differently to different segments?
Next is engagement analysis. This is how do customers interact with your product? How often do they interact with your product? How deeply do they interact with your product? What is it about your product that they like and what is it that they don’t like? So let’s say you have a website and you notice that majority of your customers have who visit the website, leave the website in a very short duration of time, right?
Let’s say you’re noticing that majority of your customers have a very short session duration. How do you use this data? Once you measure it, you have this data and now that you have that data, how do you use it to understand how you can improve engagement for your product?
Next is feedback. Feedback analysis is nothing but how do you collect feedback from various signal sources? Like could be feedback or [inaudible] ratings, reviews, all of that data and use that to understand what are your strengths and weaknesses for your product. And next is AB experimentation. This is where you show two different variations of your product to your customer and see which one resonates with your user and use that data to eventually launch the change to all the users.
And finally, machine learning. Machine learning is a very important tool that as a product manager you can leverage to give user centric and innovative solutions for your customers.
It’s important for you to know and have an awareness of what are the different machine learning models, algorithms so you can partner effectively with your engineering team, with your data science team to build the end-to-end pipeline to deliver the feature. Of these seven techniques, we will first look at funnel analysis. Like I already said, funnel analysis is a method used to analyze the sequence of events leading up to a point of conversion. Let’s say you have an e-commerce website.
Let’s look at one customer journey, right? Let’s say the customer came to your website, they searched for a product that they wanted to purchase, they added the product to cart, they went through checkout, and at which case they finally completed the purchase, right? This is just one customer, but not every customer will follow the same journey. Some maybe will come to your website, at which point they lose interest and they leave.
Some maybe will come to your website, they’ll add the product to cart, at which point they leave only a small section of customer eventually go all the way up till purchase, entering their payment details and completing, which is why it looks like a funnel. The ideal journey is obviously the whole thing. You want every customer to go through every step, but the funnel keeps getting shorter because customers keep dropping off.
Once you have this data, let’s say you measured this data for your journey for whichever feature you own, you measured the data in the form of a funnel, and let’s say you notice that majority of your customers are dropping off at the homepage, maybe you can hypothesize that your page is too slow, which is why customers are losing interest and they’re leaving. And whereas if you notice that majority of customers are leaving at the payment and checkout screen, at which point you can hypothesize, maybe the pricing is too expensive.
Once you have these different hypothesis, you can run experiments and improve the overall conversion rate for your product. Okay, next is AB experimentation. Here I have two different greeting cards for a Christmas, right? Maybe the one on the left resonates with the customers more and they click on it and they open it. Maybe the one on the right is not as appealing. Here, this is a trivial example.
In this case, the customer greetings, it maybe doesn’t matter if customers really open it and see it because it doesn’t translate into business outcome. But that’s not always the case, right? Let’s say you have an open house website, you want customers to click on the website, sign up for the open house so that your house is eventually sold, maybe in this case the color of the button results in different conversion rate and that it really matters what color of the button. That is something you can maybe experiment and see which one results in a higher conversion rate, not just for visual things.
Here I have Nike website, maybe the search algorithm on the left. There’s different from the search algorithm on the search result ranking on the right. Maybe the one on the left is resulting in higher units of shoes sold and higher revenue for the company, in which case you can totally AB experiment this as well.
What I mean to say here is that AB experimentation is not just limited to visual things, UI elements and things like that, but you could totally even AB experiment algorithms, APIs, backend systems or different systems that eventually translate into better user experience for your customer. So what exactly is AB testing? It is called split testing, bucket testing, randomized control experiment. It’s typically used to compare different versions of a webpage, but you can test anything from the color of a button to the backend algorithm to the layout of a page.
The AB groups are typically called control group and test group, and all elements are held constant except for that one thing that you really care about and you measure it. And it’s the best scientific way to establish causality with high probability. What it means really is that you’re not going by gut feeling, you’re not going by instant, but rather you’re running a scientific experiment and saying that based on the results of the experiment, I can conclusively say I can conclude that changing something results in a higher something else.
You can establish that causality in a very scientific way. What are the different stages of AB experiment is the first is you have a problem statement. You define the hypothesis, you design the experiment, you run the experiment, and then you eventually interpret the results based on the problem, based on the business that you’re in, based on the company that you’re running AB experiment. For you problem statements will be very different because you want the experiment to ladder up to the uber goal that the company has set.
Let’s say that I join as a product manager for a travel company like Expedia or booking.com. I will run experiments that eventually impact these metrics because that’s what the company cares about. The company wants to increase number of bookings, they want to increase their loyalty participation program, they want to increase maybe number of searches that people are conducting on their website.
Whereas if you are a media company like Netflix or Amazon Prime, they want to increase engagement, they want to increase subscription rate, they want to increase content consumption time. So your experiments that you run will impact different metrics. And as a product manager, if you’re running AB experimentation, you want to be very clear on the problem statement even before you get started, even before you design the experiment.
That is something you start off your ab experimentation process with. Again, if you’re an e-commerce company, your goal is to increase products viewed, products added to cart, resulting in higher conversion. And finally, if you’re a social media company like Instagram or Facebook, your goal is to increase engagement or maybe increase revenue through advertisement and things like that. Here what I’ve captured is that the problem statement could be very, very different, and that is something you want to be very clear about and define it at the start of the process itself.
Next is defining the hypothesis, right? A hypothesis is nothing but a testable statement that predicts how changing something will affect certain metric or a user behavior. So here these are the three steps that I use to define the hypothesis is you want to be clear on the problem based on evidence, and you want to decide changing something impact certain outcome and how that impacts the problem.
How do you know you have achieved the outcome is when you see the metrics change, right? Here below I have defined an example of how you could do that. So let’s say you are a product manager for an e-commerce website. You’re seeing lesser number of units sold on the website through sales data. That is the problem you have and that is the evidence you have.
Let’s say you believe that incorporating something like a social things like X number of people purchase in the last 24 hours will influence them to purchase and make the purchase. That will result in people actually converting. And that’s your gut feeling and that’s your hypothesis that you start off with. At the end of the experiment, you’re seeing whether indeed doing that change results in higher revenue and higher units sold. So that is what your null hypothesis is, and that is what your alternate hypothesis.
You can also define the significance level and statistics, power, and these are industry standards that you use a level of 0.05 and 0.8 to define the sample size that you want to use for running the hypothesis. Next is designing the experiment. When you design the experiment, you want to be very clear on what the metric is.
The primary metric, and you also want to be clear on the revenue. Maybe you have one primary metric, but maybe in this case it is revenue per user per month. But you could also have secondary metric and other metric that you want to test. You also want to determine the population that you want to test it for. Let’s say whether you want to run the experiments specifically in US in Europe for certain section of the market or all users.
Next is how many people do you want to run the experiment for is determining the sample size here I already talked about using an industry standard of alpha and power to determine how big your sample size should be in order to have statistically significant data to draw conclusion.
And finally, how long do you want to run the experiment? In this case, you could run it for two weeks, you could run it for two months. You can run it for much longer. And you also need to think about seasonality days of the week and holidays. You don’t want to design some email engagement experiment during holiday season when people are on vacation, not really checking their emails. Those are some factors you would decide take into factor when you’re designing the experiment.
Next is once you have all of these things finalized, you randomly assign users to group A and group B, and it’s very important to randomize so you’re not introducing any bias into the process. And you partner with the dev team to instrument logging for any necessary metric, collecting data to make sure you have a dashboard that surfaces the metric that you care about.
As you can see on the right, you are tracking revenue and you’re tracking how does revenue differ between the control group and the treatment group. And that will help you decide how your experiment is doing. And then you want to avoid looking at results before running the experiment for the entire duration of it and avoid peaking and jumping into conclusion. And then finally, once the experiment is run, you want to make sure that the data is reliable.
You want to perform some sanity check. If the data is obviously unreliable, you want to discard it and rerun it and then make some trade offs. Let’s say at the start of the experiment, you decided to measure engagement and revenue. And at the end of the experiment you saw that, okay, based on the changes that you’ve introduced, revenue is looking good, it’s going up, that’s great.
But if engagement is going down, you want to make the trade off that. Is it really worth introducing the change? How do you want to look at the result? How do you want to interpret the result and things like that? And then eventually launch the change to everyone. This is one way you take a data-driven approach to introduce changes.
An AB experimentation is widely used within Microsoft is something I’ve used throughout my career. We have these office bills that are released each month to millions of users, so before we introduce a change to such a worldwide population, we launch it to a small segment of population.
We collect telemetry signals, we collect all the signals, crash signals, we make sure that it’s looking good, and then eventually launch the change through a different release pipeline that we have. And that is something that throughout industry, it’s practiced in Instagram everywhere where they test some change with a small section of user, use that data to then eventually launch the change.
Cool. Next one is machine learning. Machine learning is not a magic wand, but it’s an application of AI that provides system the ability to learn and improve from experience without being explicitly programmed. When do you want to use machine learning is when you have lots of data, when you have a complex logic, something that cannot be solved with if statements cannot be solved by classic programming. That’s a good example.
When you want introduce some sort of personalization, like you have the case with Uber, you have the case with DoorDash, Instacart, all of them provide you a very personalized experience. And when you want the system to learn with time, that’s also a classic example where you want to introduce machine learning. Something like Twitter, what’s standing on Twitter today might not be training tomorrow. And that’s where machine learning is a classic example and fits the scenario.
Here I have three different types of machine learning. One is the supervised machine learning where you have machine learns from training data that is labeled where you train the system while it learns to do on its own. Next is you have non labeled training data. And finally is reinforcement learning where the machine learns on its own.
Here I’ve listed quickly different techniques of machine learning that you can use. One is ranking. This is something I already talked about that Amazon uses machine learning for, powering the search result ranking recommendation. Again, Netflix uses it for powering their home screen. Different recommendation, I guard them.
The great thing about recommendation, it doesn’t have to be perfect as long, it’s close to accurate. Customers are happy classification. Facebook uses it for tagging different users on their product. Classic example of classification regression is something we use for seeing, for casting, clustering for Spotify, uses it for clustering songs. And finally, chase uses anomaly detection for flagging fraudulent transaction. Thank you.
Sukrutha Bhadouria:
Thank you so much. This was a wonderful session. Yes, going to hop on to the next one. Thank you so much.
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In this ELEVATEsession, Dotty Nordberg (Senior DevOps Engineer at Pure Storage) shares strategies ensuring a positive outcome when presenting your ideas. You will learn how to effectively use various forms of communication (e.g. email, slack, zoom), who you should talk to (and what you should talk to them about), and how can you get those key stakeholders to buy-in to your plan.
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Thanks Angie. Yeah, so happy to be here today. First of all, I’d like to say happy International Women’s Day, everyone. Thank you for joining. This session is going to be hopefully a fun session on effective communication in particular persuasion, getting that yes, that is so critical in our work and our lives. Let’s get started. Again, we have a full agenda today. We have a short amount of time, so hopefully we’ll get through all of this. If there are any questions, I hope to get to them at the end. If not, you can always reach out. I’ll give you my contact information and I’m happy to talk after.
I’ll introduce myself. We’ll define persuasion so that we’re all on the same page. We’ll talk about why persuasion is so important. We’ll talk about some challenges that you may face with being effective in communicating and persuading with others, and then some strategies to overcome that and to increase your persuasion powers.
Then we’ll talk about a success plan for the day of say you have a big idea that you want to present to your management team or maybe even higher ups. We’ll talk about the success plan for that particular day and then hopefully Q and A. Let’s get going. Okay, so me, a little bit about me. I am a technologist. I’ve been a geek all my life. I have an undergraduate degree in math, not computer science.
I’m a little bit of a non-traditional background. I took a bit of a circuitous route here. I started out as a Windows systems administrator. I got some certificates, so those bootcamps and those certificate courses, they can help you get your foot in the door. That’s how I did that, and then I worked on the Linux side of things as a Linux systems administrator. Got some training in that. Again, certification courses, working kind of on my own, highlighting that on my resume and at interviews and things like that.
Now, my focus for the last several years has been more of the cloud platform engineering and systems administration that, so as we mentioned, I’m a DevOps engineer. My current role at Pure Storage, I’ve been there for about five years, really enjoy it a lot. Moved to the San Francisco Bay Area about 13 years ago. I was originally on the east coast of the US, grew up in New York, lived in Atlanta for a while and then moved out to the west coast of the US near San Francisco about 13 years ago. I’m also a speaker.
I’ve really enjoyed speaking at events like Grease Hopper / Anita B, and ACM-W, and then of course Girl Geek X. I’ve been a mentor to probably hundreds of techies at this point. Mostly people new to tech. And they’re so talented, so inspirational. I cannot wait to see what they do next. And it is one of my favorite ways to give back to this community. I mean it’s small, but I think every little bit helps, so it’s one of my all time favorite things to do. Other miscellaneous things about me. Little fun facts.
I like to run and hike. I’ve trained in martial arts. I like to read. I’m in a couple of book clubs, travel, and right now I’m learning Spanish just for fun. I am a lifelong geek because I mentioned I love science and sci-fi. I dreamed of being an astronaut. And one quick little story about that here out where I live is, right down the street is one of the NASA research centers. A couple of years ago, one of my friends said, “Hey, I’ve been volunteering at the NASA Center there. They have an educational program for 12 year olds and 13 year olds. Do you want to to do this with me? I hear you want to be an astronaut. “And I’m like, “yes, please sign me up right away.”
It was so fun as a temporary volunteer, I got a temporary badge to just go right through the gates. The guards just kind of wave you right through the gates, which was so fun. And then at the educational center working with the kids, they had four or five different stations that were teaching the kids all about space, space, travel, it makes it possible, flight, all of that stuff. Release principle for flight and orbital mechanics and all that stuff like that. One of the displays is a mock space shuttle mission with a mock little space shuttle. And then I got to be Houston. I got to be ground control and be like, “Hey, ground control, mission control to space shuttle, please come in, space shuttle.” I was like a twelve year old kid at that thing. It was great. I think I had more fun than the kids did that day, so a lot of fun.
Okay, so let’s get to our topic today. Persuasion. Looking up on our friend dictionary.com, it says that persuasion is the act of persuading or seeking to persuade. The power of persuading and persuasive force – which really doesn’t tell us what persuade or persuading means, so what does persuade mean?
Persuade is to prevail on a person to do something by advising or urging to induce to believe by appealing to reason or understanding, convince. If you combine the two, it looks like persuasion is convincing the act of convincing someone to do something or one of the things that while I was doing this research on persuasion is it kind of seems similar to negotiation, but there is a difference in negotiation looking at the definition of that there’s a mutual discussion and arrangement of the terms of a transaction or agreement.
The difference for me is that persuasion is kind of a one side is trying to convince all the other sides of something, of the value, of their idea, of the reason why we should do this In a negotiation, it’s all parties. They’re trying to benefit in some way. For example, in a job offer, the company is trying to convince you that they’re a great company to work for, they have great benefits, they have great tech that you’ll be working on amazing products, things like that.
And you, for your part of that negotiation of the job offer, you’re trying to convince them to pay you as much as possible to pay you what you’re worth, say a million dollars a year, something like that. If you figure out how to do that, please, please let me know because I still have not done been able to do that yet. I would love to. Then contrasting that with a persuasion. Say it’s a company crisis. Things are on fire, it’s a P one, it’s outage. Services are down, customers are complaining. You really need to kind of maybe push your idea and say, Hey, this is the right way to go. You don’t really have time to negotiate per se.
And why is this important? It is a soft skill, meaning that it’s not a technical skill. It’s not like learning Python or Java or something like that, but it’s not typically taught in schools or in life in general.
Soft skills are very, very important tools to have for your career or even in your life. We use this a lot. I would say we use it in the workplace as well as in our regular lives. When we’re talking to, say maybe we’re on a board of a city council or something, and you’re speaking to legislators, you need to be able to persuade them like, this is the way to go, or this is not the way to go. Even parent teacher meetings, maybe your child needs a little extra help in class or you are the student and you’re working with your professors, asking for more time on a project, things like that. And for those of us with kids, I’m sure we use persuasion pretty much every night trying to convince our kids to go to bed at the appropriate time.
Persuasion is needed when you have a new idea, when you have a different opinion than others. When you’re working on those key assignments and you need to get a direction on which way to go, it could be the wrong direction to start, but sometimes you just need to get going, especially when you’re asking for a raise or a promotion. Definitely need to figure out a way to persuade your manager that, yes, I’ve done X, Y, Z, here’s the market rate for what I’ve been doing and things like that. And I highly recommend you do that as at least once a year, every one year or two years, something like that.
How do we use persuasion? We use it in meetings, we use it in presentations. We even use it in email over Slack. And especially as we discussed in a crisis situation, some possible personal challenges might face or I think you have, we hear a lot about imposter syndrome that when you feel like you don’t belong because you don’t have the skills, why am I here? They’re going to find out I’m a fake, I’m a fraud. I shouldn’t be doing this.
Maybe you feel like you’re the only person in your group of you’re the only woman say, or the only person of color, the only LGBQ, whatever that is for you. Or maybe you’re cross section of a couple of those that might be intimidating for you to try and put your ideas forward. Bro, culture is a thing and that might intimidate you as well, especially even cultural differences and societal norms.
Say you’re from a different country than most of the folks on your team. You have different cultural expectations and things that might hold you back a little bit. There is good news.
If you feel any of these things in particular, imposter syndrome, you are not alone. I’ve talked to many, many folks in all levels of companies, directors of engineering for 5,000 person company, and all the way down to individual contributors and affect men, women, all genders.
Everybody feels imposter syndrome at some point, especially if you’re the new person or if you’re new to the industry. If you’re new to the company or new to the industry, you are going to feel this way. Keep in mind that it’s pretty comforting to know that that’s normal to feel that way. Nobody expects you to know everything right away, especially if you’re new. And yeah, like I said, we’ve all been there, so take comfort in that and know that you’ll be fine.
You do deserve to be here and we want you here. You’ve earned your place and you do deserve to be here. More good news is that more companies are recognizing the importance of diversity, equality, and inclusion programs. And some have sensitivity trainings that are required of their employees.
Overall, I would say these challenges are diminishing, and I’ve been in this industry for many, many, many years and I’ve seen for me personally, these challenges going away, which is good news.
Here are our strategies for increasing our persuasion. Tailor your message for the different situations that you’re in. Is it a crisis or is it a non-emergency? It’s a crisis. It’s going to be a very different conversation than if you, it’s a non-emergency and you have the time to think and maybe plan out the project and things like that. Are you talking to a teammate?
Are you talking to your manager? Are you talking to the CEO of your company? Very different conversations because just for the view of that person, the executives are going to get the 10,000 foot view versus your teammate who’s right by your side every day. They know the lingo, they know everything that you’re doing. That’s going to be a very different conversation. Even your manager, they’re looking at it from a different point of view than you are. They know the tech, but then they also are a little bit higher in the hierarchy of the company, and so they have a little bit different view of things.
The words that you use and the message would be a little bit different. Is it going to be in person or video conference? Is it going to be over email or chat? Is this person a tech geek or are they not a tech geek? Meaning, are they in your industry? I mean every industry, its own geek speak, I would say. Is this person part of that community or not? It’s going to be a different conversation if say, I as a DevOps engineer, I’m talking to a finance person or hr, something like that, so you tailor your message to all these different situations, try to get into the other person’s head and understand their point of view.
Anticipate any objections to your idea, try and see the issue from all angles. This will foster better communication with those people that you’re presenting to, assuming that you have time to put together a presentation and it’ll form a more comprehensive case for your idea as well. Master the art of storytelling, so try to share your ideas through compelling stories and then interesting narratives, capture the audience’s attention and do a time check.
Right now we only have a couple of minutes, so I’m going to zip through these last few slides pretty quick. If your first effort, first your audience is not persuaded, keep an open mind. Ask questions to decipher their point of view and restate your idea in a different way.
Use your logic and reasoning. That’s super important. If there’s time, practice, practice, practice, research and rehearse your key points. Pets, make a great practice. Partners, dogs, maybe more than cats. Start small.
Use one-on-ones with coworkers or teammates and build support before the big presentation day. And then at the end of the discussion, even if it’s a crisis, make sure that everybody understands the idea and the decision makers have enough information to proceed with their decision. And if there’s follow-ups, make sure that you address those and do the work needed for those.
Okay, so it’s the big day of the presentation. Remain calm. Use your appropriate body language. Like, if you’re presenting to a room, stand up straight. Try and keep your hands from moving around too much. And these are reminders for myself as well. Use your compelling stories with your logic, your reasoning, and your credible sources.
Make sure that the decision makers hear you and you address any concerns that they have. Ask questions if you need to.
Try and understand their point of view, especially if they don’t agree with you right away. And keep a positive and curious attitude after the presentation. Take a deep breath and congratulate yourself even if your idea isn’t implemented. I feel that it’s a win for you because you’ve shown that you’re passionate and you’re creative.
And the next time you present an idea, the folks that have heard you the first time, it’d probably be similar folks. They’ll understand and they’ll say, “Hey, Dotty, she has pretty good ideas. She’s really excited about what she does and she’s creative. Let’s hear her out this time.” I think that that is a positive for you and a win. And plus, you’ll have experience as well and be able to get your feedback from there and tweak your presentation for the next time.
And then with that, I would like to thank you for attending. If you have any questions, please reach out to me over LinkedIn and I think we’re going to be cut off soon, but thanks so much everybody. I hope you’re having a great time at the conference.
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In this ELEVATEsession,D’Janae Robinson (Chief of Staff at RHJ Consulting) defines what being the first means as a framework, helping identify how it shows up in lived experiences. She shares how the challenges impacting lived experienced (e.g. workplace, family, society), and helps you conduct an inspiring self-analysis of ways to conquer challenges.
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Good afternoon everyone, and thank you so much, Angie, for that introduction. Happy International Women’s Day. Let’s engage in the chat and tell me one word that describes how you’re feeling today on this Friday. Maybe even three words just to describe how you’re feeling on this amazing day, being a first generation and conquering the unforeseen challenges that arise when breaking generational curses.
For this topic, I want to not just focus on being a first gen in the aspect of academia. I also want to bring in those who are trailblazers. You are the first in your family to navigate a specific occupational space.
Maybe you’re the first entrepreneur, maybe you’re the first in your family to navigate a tech space, or based off of how you choose to identify in all your intersections, you’re the first in your family to say, you know what? I’m picking my career first.
I made the decision. I don’t want children. And that is such a foreign concept, right? As the woman is how I identify, and especially in my community to pick career. Why would you want to do that? Why would you not want to have kids? It’s just a personal choice.
As I navigate through this presentation, I want you to also consider, I am talking about yourself as well. Even though the focus will come from my lived experience, from the small perspective of being a first generation, a two-time, first generation college graduate, before I move forward, I want us to ensure as a diversity equity inclusion specialist that we create a psychologically safe space.
I am here not to change how you were raised, not to change how you believe or not to change your lived experience, but I encourage you to approach this conversation from a different lens and perspective and understand that everyone that’s sitting at this equitable table that we keep talking about has a different lived experience than you.
As I navigate through this conversation and we’re all engaging in the chat to understand that your perspective is valid and so is someone else’s, here are three ways that I conquered and navigated my challenges. We glorify and glamorize being the first. Being a trailblazer. We glorify and glamorize promotions.
Whether you’re the first woman in a specific role, the first non-binary in a specific role, the first outwardly, whatever the case may be, we praise them, we cheer you on. You even have cake sometimes, or a nice fancy plaque.
We do not talk about what comes with the weight, the baggage, the expectations that come with creating this pathway, being the first, being the trailblazer. You are the blueprint.
For me, as a two-time, first generation graduate, my mental health was impacted. I don’t know what it is about getting a secondary degree or being the first, but here I was in a university in a school after already experienced corporate America and came back and I felt inadequate.
I felt like I wasn’t qualified because when I looked around this table, I was the only one who looked like me. I was the blueprint, but yet I was looking for my mentor. I couldn’t call my big mama. In my family, being the first, I couldn’t call my auntie. I couldn’t call my uncle and say, big mama. When you were 25 navigating your secondary degree, what did it do? What did it feel like? What steps did you take and how did you take care of your mental health?
This is what I was able to do. I was able to get monthly massages. One, getting massages, eliminates stress within the body. I was so tense. I was also dealing with weight fluctuation. My hair also was falling out. I did this on purpose. But way back then, in 2020, I believe my hair started falling out.
That is how stress attacked my body. Engage in the chat. How does stress attack your body? How have you navigated your challenges in the endeavor that you embarked in? Monthly massages was one, an accountability partner. I needed a safe space to go to. I needed a friend. I needed a person to call and say the things we ought not dare to say, we should be proud to be the first.
We should be proud for that promotion. But they don’t talk about what comes with being the first. And I was calling her and saying, friend, I want to quit. Today’s the day I want to give up. I can’t do this anymore because I’m searching and I’m searching and I’m looking for someone to tell me I’ve been there. D, just keep pushing.
An accountability partner, they weren’t there to problem solve. They were just there to say, close your laptop, go take a walk, go get your massage, schedule another massage. I love the good cry out method, so just cry it out the other way that it impacts me. Being a first generation of trailblazer, imposter syndrome and me were like, peanut butter and jelly, salt and pepper, green eggs and ham imposter showed up in the work.
Working in two of the top tech companies and being the only one, sometimes that looks like me on my team, I felt inadequate because when you don’t see people who look like you in spaces that you aspire to be in, it can be hard to believe that you’re qualified and equipped to be in a specific role, to be in a specific academia space, as well as my family being the first, I was looking around at my family and saying, nobody else has navigated this path. So maybe I’m weird, maybe I’m different. Maybe I shouldn’t pursue a different path because I’ve never seen anybody else done it.
What did I do internally in the corporate space, I found support groups. I found internal ERGs, employee research groups that I can relate to with other first generation graduates who I was able to identify with and ask them how did they navigate their path as well as therapy. Within the family space, I don’t know about your family, I just can talk about mine.
Going to therapy was still foreign, it’s still taboo. And I’m 30 years old now, going to therapy to seek psychological help, to help me remove whatever that imposter syndrome was in my body. I had to go back to my childhood. Why did I feel inadequate? Why did I feel like I had to work so hard to obtain something to where I was still the only one in the room? And last but not least, my faith as a unapologetic God-fearing woman.
Let me tell y’all what, my faith was tested in a way that had never been tested before. Why? Because I no longer had the environment to look at folks that I wanted to be like. This was all self, this was all about me. I was the blueprint.
I had to call capital GOD, and I said, look, man, this is crazy. You want to pick me? But it’s never been done before. So my prayer life had to increase. Now, if you are not a believer of capital GOD, that is absolutely okay. If you are a believer in energy, in crystals, a higher power, a higher source, I encourage you to tap into that in the moments where you want to give up and the moments where you feel like you shouldn’t be here. And the last thing that I was able to do was I had to trust.
I had to trust that God put me in this place for a reason to be a light in rooms full of darkness. I was called and I had to trust him that I was here to help other women, other non-binary individuals, and to look back and to be the representation I never had. When the younger version of myself comes and says, DJ, I need your help. How did you do it? I can help them.
I understand the power of visual representation and seeing yourself in spaces that you’ve never been in is the motivator. If you look at the top left on my screen in the blue chair, that was the first photo of me working at a Fortune 500 company and being the first in my family to work at a tech company, the photo right below it with me crying in my graduation camp, hugging my aunt, the first of my family to graduate with a bachelor’s degree, the top center photo.
I am chief of staff of RHJ, consulting industry, excuse me, consulting company, and I got this position at the age of 29, so I’m also navigating ageism. I am the youngest person in this role, leading a team of folks who are older than me, but the first in my family to hold such a C-suite level position. The middle bottom one is HBCU. I’m a proud HBCU graduate. Shout out to the HBCU graduates that are on the call. Drop what school you’re representing. I’m representing Houston Tillison University based in Austin, Texas. It is the oldest institution of higher learning in Austin, Texas, and the only HBCU in Austin, Texas. Last but not least, the last picture on my right hand side with President Collette. She was the first black female president of the illustrious Houston Tillison University, and I took a picture along with her as I was the first in my family to obtain my master’s degree.
Now you can see that you’ve become the blueprint. Find people in your community, people not in your community, to be allies. To let you know I’ve done it too. I’ve been a trailblazer. I’ve been the blueprint. And in closing, key takeaways be the representation.
You are the mother you never had. You are the auntie, the brother, the sister, the niece, the nephew. You are the representation you never had. And then instead of thinking, why me, I encourage you to change that perspective and say, why not me?
Thank you so much for the opportunity to share a little bit of my story, my testimony, my lived experience on breaking generational curses and navigating the challenges that occur when you are operating a new path and you’ve become the new stigma, the new representation of your unapologetic self.
Lastly, please connect with me on LinkedIn. If you take your phone and scan the QR code, I would love to connect with everybody. Happy Women’s History Month and Happy International Women’s Day.
What opportunities and challenges have you seen hiring a tenured woman leader where Gen X candidates compete with a younger pool, millennials or Generation Z that might not understand their first, how to align differences?
Yeah, I’m going to share my lived experience as being the youngest in leadership roles. The opportunities that come with that. One is experience and finding allies, an ally.
An ally also doesn’t mean someone that looks like you, but it also could mean someone within your community, so I encourage you, Anna, and please let me know if I’m answering this correctly – Find folks who are willing to drop your name in rooms in which you otherwise wouldn’t have been in.
The reason why also along with my faith that I’m able to be a chief of staff in this position is because myself and a male ally shout out to male allies, he saw that I had a large amount of transferable skills.
I was just missing key variables that I otherwise wouldn’t have access to unless I was at the table, so those are the opportunities, the challenges, and I’m going to be so blunt and transparent, the challenges that occur by being the youngest is people not taking you seriously.
People thinking that you are inadequate or you do not have the knowledge because of your age. Ageism is a spectrum. Whether you are on the, I call ’em wisdom, folks of wisdom, or you are growing in your career, you may have different experiences.
For me, the challenges were I wasn’t taken seriously and I received questioning of my knowledge and expertise in a way that I haven’t seen other individuals on my same team who align in the same age experience.
Angie Chang:
Thank you so much for that talk. That was very inspiring.
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In this ELEVATEsession,Candice Quadros(Director of Program Management & Productivity at Roku) spoke about building an understanding of the Technical Program Management or “TPM toolbox”, creating an actionable plan for switching into TPM careers, and growing your TPM career.
Technical Program Management (AKA TPM) is a booming career option for many entry-level, mid-level and executive level professionals. Now, more than ever, TPMs are in high demand across the tech industry.
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Thank you so much, Sukrutha and the Girl Geek X community. Happy International Women’s Day. I hope you’re celebrating this day in your own unique way. My name is Candice Quadros and a very big welcome to all of you to my session today and my session is, “So you want to be a technical program manager.”
Most of you might have probably heard about the technical program management role and even general program management roles. They’re pretty much the buzzwords in the industry nowadays, and it is really a booming career option for employees no matter which way you are in your career, whether entry level, mid-level, or even executive level more than ever before.
I have seen so many roles for technical program managers as well as program managers in the tech industry, and I would love to share with you a few of my learnings with you today.Let me flip this slide. I’m currently at Roku. I’m the director for program management and productivity. I’m based in San Jose in the Bay Area. Prior to working at Roku, I’ve been in TPM leadership positions at Google and Microsoft.
Pretty much my entire 15 year career has been in the tech industry, but I didn’t start out as a technical program manager. In fact, I started out as a software developer at Microsoft. But early on in my career when I was interacting with the technical program managers on my team, I was really interested in the work that they did and I wanted to do what they did.
It was a long journey for me before I could make the switch from software development into technical program management. And this long journey had many steps and many, many missteps along the way. At today’s session, I hope to give you an overview of what it takes. What are those key skills that you would need to master so that you can make the switch into technical program management? And after you make the switch, how do you succeed in the technical program management discipline?
For the agenda today, for all of the aspiring TPMs and for all of the TPMs that want to grow their career, we’ll start out just defining what do we mean by TPM? And then understand what separates TPM from program manager and from project manager and what this means on a day-to-day basis. We’ll then dive into what I call the TPM toolbox, and these are the core tools or the core skills that are really the key to success in the TPM discipline. And finally, we’ll take a look at steps to getting that dream TPM job and how to be successful as a TPM.
To kick things off, a TPM is the one who creates the program strategy and creates the program goals. Then you are able to articulate that program strategy and those program goals, and then you are the one that passionately owns that strategy.
Once you own the strategy, you are the one that’s finally driving the program to completion and being relentless in getting to the program delivery. Driving to completion may mean many things. It could range from the simplest stuff, which is driving a meeting or to the more complicated stuff, such as aligning strategy or getting buy-in from executives.
The metaphor that I like to use is a TPM is just like an architect for a house who comes up and draws up the blueprints. The architect isn’t the one that’s building the drywall or installing the plumbing, but they are the ones that make sure all of these different projects come together to create that strategic vision, which is that beautiful house at the end of the day. Similarly, the TPM’s role is yes, you are responsible in a way for individual projects that come together, but you are thinking beyond these individual projects. You are thinking about long-term success, long-term strategic vision, and long-term realization of those business outcomes.
Before we get into the TPM skills, I also wanted to briefly touch on what separates TPM from general program managers and from project managers. You’ll see all these roles when you’re looking for a job, you’re going to see all these different roles in the job market, but each of them have key differences. And of course the expectations also are different for each of these roles.
If I had to say things in a nutshell, a TPM is the one that’s focused on delivery of technical programs, program managers focused on delivery of general programs. And the program itself is a group of projects and project managers are the ones that oversee these individual projects. The TPM role, in a sense, the TPM role encompasses all of the work that project managers do and program managers do.
The TPM adds in their own technical expertise. They are the ones that understand the technical area or they have that domain expertise. They’re able to speak the technical language, they’re able to identify and mitigate technical risks or technical issues. And TPMs and program managers, they’re the ones that focus on the long-term business objectives or long-term strategic goals. And how do groups of projects that build up a program, how do those groups of projects get us to achieve the strategic vision? Typically, TPMs and program managers are focused on the strategic vision, whereas project managers are focused on these individual projects and they’re focused on delivery of individual projects.
Again, the definition of a project varies from team to team or even company to company. Across the tech industry, you’ll see a variety of definitions for these roles as well as the project. There is a lot of nuance in what it means to be a TPM versus a program manager versus a project manager. But in general, this is kind of the framework that I go by when I’m trying to explain the differences between these roles.
Let’s step back and just take a look at what a day in the life of a TPM involves. And a lot of this might apply to the general program management role as well. The day in the life of a TPM typically involves daily management through the lifecycle of the technical program or technical project.
The TPM is the one that defines the program control. They define the processes, any kind of procedures, reporting, whatever you need to manage that technical program are defined by the TPM. They plan the overall program schedule, they plan out the milestones, they also monitor progress of the program with respect to the schedule and the milestones, so making sure that we are meeting the milestones that have been defined. TPMs also are able to identify and manage risks and issues that may arise. And these always do arise in any kind of program, especially in technical programs during the course of the program lifecycle.
The TPMs are the ones that have the capability of doing a thorough risk assessment, taking into account any kind of technical risks and issues, and then developing strategies or mitigation plans to correct these risks or issues or mitigate them as they occur. TPMs also coordinate dependencies between various different programs. There might be different engineering team working on programs that intersect each other in some way or have dependencies on each other. They’re the ones that coordinate this between the different teams, so identify and have that big picture view and understand what are the needs across the various teams that are partnering and then being able to come up with a dependency management plan. In some cases,
TPMs are also responsible for the resources that are assigned to the program. They manage and then they’re able to use the resources as necessary for successful delivery of the program. TPMs also sometimes end up managing stakeholders who are involved in the program, so these stakeholders might range from executives to individual contributors across the various teams. And then the TPMs are the ones that make sure the deliverables are lined across the program. That’s a lot of stuff.
This is in the day in the life of TPM may involve. All of these are a couple of these on a day-to-day basis. But you are essentially breathing, living, breathing, eating the program that you’re running, and you have a clear idea of what is the goal and how far are you away from the goal and what is it going to take to get to that goal.
All right, so now we can get into really the meat of the presentation today, which is the TPM toolbox. Being a TPM myself and being a TPM for pretty much my entire career, I really believe that delivery of great project or delivery of a great program is pretty much in the hands of one person, which is the TPM. It might sound counterintuitive to a lot of people because there is always a team that’s involved in this process. It’s not just one person that you would say, oh, you’re the one that’s responsible. There’s a whole team that’s part of this. It’s true that the team members are, each of them have a role to play. Each of them are crucial and really important to the success of the program. But the TPM is the one who takes on the responsibility for delivery of the program and outcome of the program.
The TPM so is always thinking about what are we trying to do with this program and how do we get there? The TPM is really burdened with the great task. They’re the one that, so you need a lot of skills and qualities and you need to build and develop these skills and qualities over the years and competencies over the years.
All the successful TPMs that I’ve talked to or I’ve worked alongside with have a TPM toolbox, which they use to run their programs and achieve those results. Let’s take a look inside what’s in this toolbox and what this is my perspective on what I think are some of the key skills that are needed to be a successful TPM.
Starting with the top level, every toolbox has the top layer when you open it up, these are the things on the top. This is where your most important, your most used tools are stored. These are the ones that you have to pick at. They’re the ones that you need handy. You’re always picking these tools out of your toolbox.
Having these can make or break your project or your program. The first one, the tool one that I consider really key is ownership as the TPM. Like I said, the TPM is the owner of this program. The TPM is the one that has to think to long-term think strategic. So when you are making trade-offs, you are thinking about the long-term value and not the short-term results, so you don’t sacrifice long-term value for short-term results because you are the owner and you’re thinking like that.
As the owner of these programs, you’re acting on behalf of your entire company. It, it’s not just you or your team, you’re thinking about the whole company. And also being the owner means as the TPM, you are never going to say, that’s not my job.
You are the owner. You’ll do whatever it takes to get your program to delivery. When I run my programs at Roku, they typically, the program teams range from five people to 50 people. And as an owner of these programs, I consider each of these people in my teams as resources that I can use and deploy to achieve the program goals.
Second thing I’d say here, tool number two is effective communication and high EQ. The successful TPM has to know how to communicate effectively because every person perceives information differently. Some people like numbers, they like data. Other people like to see the human side or the human outcome of an issue. The TPM has to be able to understand these different aspects and adjust their communication style. TPM also needs a high EQ that helps them assess their audience and tailor their communication.
The tool number three here is bias for action, so TPMs need to have bias for action because speed is really, really matters in business. You to be able to understand the difference between reversible and irreversible actions. There are many decisions or actions that you can take which are essentially reversible. These do not need extensive study or extensive research, but you should be able to take calculated risk taking so that you can keep your program moving forward.
Next level, in the toolbox of the middle layer, these are the tools that are really important for successful execution of your program. These are kind of TPM or program manager general competency.
First one is planning and tracking. A goal without a plan is just a wish. This is a very common phrase that’s used in the TPM world. What this means is, yes, you may have the goal, but if you don’t have a plan and you don’t have a path to get there, it’s never going to be a reality. A successful TPM needs to be really fluent in the language of planning. You have to be able to build a plan, build the milestones, and then track those milestones along the way. Add any kind of data deadlines, KPIs that give you and visibility and an indicator into how your program is functioning. Once you have the plan in place, you have to be able to do the tracking. The KPIs are the ones that can help you see how your project is doing or something is missing.
Tool number two is the ability to dive into the details. As a TPM, you should be able to operate at all levels. You should be connected to the details, but you should also be able to take a step up and say, okay, what is the big picture view? And you should be able to understand the data and then be able to dive into the data, be able to question and challenge the data, especially when the metrics are saying one story and the people are saying another story. As the TPM, you are the one that’s diving headfirst into the details and being able to trust and rely on your team, but also challenge them when the data doesn’t back up what they’re saying.
Then, tool number three here is team management. It’s really about you should be able to manage a team of people and bring them along with you on this journey to get the job done. You absolutely need to have the right people to complete the program successfully. You should able, if you have the opportunity to choose the people, you should choose them carefully. What are the types of people that you want to have on your team? What are the skill sets you’re looking for? What is the behavior that you want to see these team members displaying? When you have the team assembled and selected, you should be able to lead, be prepared to lead and manage this team. In the beginning of the program, typically people will need more guidance and explanation, and you would want them to buy into the vision of the program.
As you go along, conflicts are going to arise. People have to learn how to work with each other. This is a novel part of program execution. Nothing for you to be afraid of, but it’s something that you need to own. As the TPM, your job is to help your team through these conflicts, bring the conflicts out of the open and help the team resolve the conflicts. And then once you’re past that is when the team members should know how to work together and will be performing at their best.
And finally, the bottom layer. This contains many tools which you are maybe not going to be using on an everyday basis, but they are going to support the tools in the top two boxes. First one is time management. For your program to be on time, you have to be on time. That means you have to have proper planning. Conflict management is number two. This is a natural part of team formation. And number three is delegation, so just like any other type of management, you should have the ability to delegate your program to different people on the team.
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During an inspiring keynote at 
