On this week's episode, Lisa Thee sits down with Sadie St. Lawrence, the founder and CEO of Women in Data. As a Coursera instructor, Sadie has trained over 300,000 people in data science, and she has led multiple analytic transformations in corporate America and beyond.
Listen as Lisa and Sadie St. Lawrence discuss Sadie's unique path to leadership in the field of data science, the future of AI, and the importance of building community.
Women in Data is an international nonprofit that is connecting the world of data scientists to each other, to expand the impact that we can all have. www.womenindata.org
ABOUT THE PODCAST
At the crossroads of uncertainty & opportunity, how do you Navigate Forward? This podcast focuses on making smart choices in a rapidly changing world. We investigate the challenges of being at a crossroads (societal, business, & personal) and finding the opportunities that arise out of disruption. Listen in on future-forward conversations with the brightest luminaries, movers, and shakers. We dig into topics such as elevating the human experience, evolution of business, workforce of the future, and fascinating technologies powering the age of digital transformation. Let’s Navigate Forward together and create what’s next!
Sponsored by Launch Consulting
Navigators in the Age of Transformation | launchconsulting.com
At a crossroads of uncertainty and opportunity. How do you navigate forward? This podcast focuses on making smart choices in a rapidly changing world. We investigate the challenges of being at a crossroads and finding the opportunities that arise out of disruption. Listen, in on future forward conversations with the brightest luminaries, movers and shakers, let's navigate forward together and create what's next. Hello everyone. And welcome to the Navigating Forward podcast. My name is Lisa Thee. I'll be your host today. I have the honor of introducing you to one of the most energizing luminaries in the field of data science today. Sadie St. Lawrence is the founder and CEO of Women in Data, an international nonprofit that is connecting the world of data scientists to each other, to expand the impact that we can all have. As a Coursera instructor, she has trained over 300,000 people in data science, and she has led multiple analytic transformations in, in corporate America and beyond. So with that in mind, I would love to welcome Sadie the podcast. Thank you for joining us today. Thanks Lisa. Excited to be chatting with you today. It's a pleasure.<Laugh>. So Sadie, uh, can you tell us a little bit about your background? Where are you from and what do you think are some of the, the themes of your childhood that have helped shaped the direction you've taken? Yeah, first I always say that hindsight is 2020. So, you know, I don't know if it's that we look back and we pull the pieces of our story that relate to who we are now, or it's vice versa. I haven't quite analyzed that problem enough to say if one is directional and causes the other, or if we pick the ones that add to our story. But when I think about my child, it was a very curious and exploratory childhood. So I grew up on what we would call in Iowa, an acreage and people ask me, why do you not call it a farm? And I said,'cause it's only nine acres and that's not big enough to classify as a farm, but people in California would consider that a farm. So I grew up on a farm in Iowa with six siblings, you know, multiple cows and cats and goats and chickens and all of that. Um, and it was really just a curious childhood. I was homeschooled. So I had a lot of time to really just explore. And given that I had so many siblings, I think my parents were very free in terms of, of how far they let us wander into the woods. And when I think back at some of the things that I did as a kid, I remember really vividly watching, um, grasshoppers. There was a whole like flock, I guess, of them that came to our patio and I was watching them for an hour 'cause I thought they were or having babies because of the way to find out insects do not birth babies. <laugh> those, it's what that would be - what mammals do. And so just like always looking tearing apart leaves, I would save chicken heads and dissect them. Um, just a very, very curious, um, individual. That makes sense to me how you would be drawn into the data science field then cuz there's a lot of pattern recognition in that, right? So you were an observer of the natural world, seeing how things were connected, seeing how things were influencing each other and developing that deep curiosity and being able to hear your own wisdom by being in an environment that wasn't filled with technology and distractions and, and structure. Right. Exactly. Yeah. I really, um, reflect on those times and sometimes wanna bring more of it back into my life. And so, you know, the struggle with that was that I didn't have an am record though. So I always knew I wanted to go to college and educate myself. Um, but I didn't really have any record. The first test I ever took was my ACT test. Um, it didn't, it didn't go very well considering that the first test that ever taken in my life, but thankfully I played the piano and was able to get into, um, college I, a piano scholarship. So they said, Hey, you have this skill. So we'll let you in. Uh, which was fantastic. I really loved exploring music. But when I was in university, I really then fell in love with science. It was my first time taking science classes and um, soon switched over my major to psychology and started at working in neuroscience labs. And really for my full undergrad was with the focus that I would go into neuroscience, get my PhD in it, be a researcher, be a teacher, et cetera. So when I was finished with my bachelor's degree, I was working in a neuroscience lab, um, studying emotional learning and memory. And here I am working on rodents, you know, having to go into the lab every day, take care of them, run the experiments and studying their emotions. And then at the end of the week, when we are done with the rodents, you know, you've taken care of them for however long of the experiment, you have to euthanize the rodent. And you know, at first it was like in the name of science and just, you know, interested to learn. But there was a moment when I held the rat in my hand and it looked me in the eyes and we had like this connection, like I'll never forget, like when our eyes locked and I just realized like, oh my goodness, you're a real living thing. And I'm about to kill you. And like I've been studying that you even have emotion. Like how am I even able to do this? So I have this whole like coming, coming to Jesus moment of like, what am I really doing? So took a step back and said, okay, well what parts of the job do I like, what do I not like, I love the, I love the data side of things. I love, you know, running experiments. What do I do? Not like working on rodents. I don't like how slow academic academia is. I don't like, no, one's gonna probably read my papers other than if you're in academia.<laugh> so thankfully I, you know, we had internet at that time and had Google, my most trusted advisor somehow stumbled on the term of data science in 2014 and was like, yep, that's it. This is what I'm gonna do. Quit my job at the lab and got my first role in corporate America and started my master's degree. What a fabulous story. And I, it, it connects on some of the through lines of how we've been able to build some op opportunities together. The AI ethics field is absolutely on fire right now. And having people like you that have had that real world experience of saying, I, I know this is what it takes to be successful in my field, but the, the table stakes are a bit too high for my own core integrity. Mm-hmm <affirmative> I think is incredibly valuable to bring it in out of an academic setting and into an in-practice setting. Because as we know, as people that have been deploying AI solutions in the wild, we don't know what we don't know yet at. Yes. Um, and then secondarily, I think it's super interesting as you talked about looking at your childhood and wondering, you know, chicken or the egg, what it include, what, how it led you here is I think it's really telling, um, that a data science wasn't even a field you could have studied. Had you gone to school at that time? It didn't exist, right? No. Yeah. And I think that's, you know, a lot of times people will ask me like, what should I be teaching my kids? And I'm like, well, this career didn't even exist when I was a kid. So probably what they're gonna do isn't even gonna exist yet. So you really have to get back to those core principles of like curiosity and critical thinking and having good ethics. Like those are the things that aren't gonna go away no matter what. And that will make them adaptable to whatever the jobs of the next century are. Absolutely. I think that this foundation relaying for the next generation has to rely on persistence, flexibility mm-hmm <affirmative> and the, to shift as the world's needs are shifting for how people engage in employment. So you've had an amazing career journey. I'm not sure how you stuffed all of your accolades in, and the time that you've been doing this since 2014, can you help me understand what has inspired you to become more of a leader in your field? I mean, now it's one thing to aspire to do it for yourself and get those traditional marks of success, but you've really invested in lifting a whole community up with you. Can you share a little bit about what led you down that path? Yeah, so I would first just say necessity is the mother right. Of the mother of invention and creativity. And when I went into data science, I like most people didn't research the demographics of the field to be like, oh, is this a community that's like me? And looks like me. It was like, no, I'm passionate about this. I wanna do this. I'm gonna do it. Well, I did it. And I got into it and I realized, wow, where are all the women in this space? And why is there not more diversity and inclusion in this space? It was a very welcoming community from open source code. And I really loved the sharing of knowledge that was in it. Something that I didn't experience as much in academia. And so I'm like this community loves to share knowledge, but we don't have a representation of the population. And it was really concerning to me because I could see that the path forward for businesses, everyone needed to be data literate. And so if, you know, while we're laying this groundwork, if that wasn't representative, like we were gonna have major problems and inequalities and all this hope and promise of what was coming with data and technology and AI was not looking so bright in my mind. And so, you know, it really started, which is like my own personal need for communities. If I was gonna survive in this field, I knew I needed to be surrounded by others, um, who were diverse thinkers and who also had that same passion, but also with the vision of like, how do we make this field and this industry more diverse in general. And so I really look at it as those two sides of the coin, a personal immediate need of like my need for community and then a vision of how, what would I love the future of this space to look like? Yeah, I very much share your point of view that data science and AI is gonna have a disproportionate effect on all aspects of human life in the next 20, 50, whatever you wanna say years. Um, it's start, we're starting to see it in certain place and it will emerge into all vectors. And so having a representation of the people that are designing the systems that are frankly doing black box thinking in most cases is incredibly important. So you can see it from different lenses and mitigate any UN unplanned risk, um, that can come along. When you have uniform thinking with uniform exp experiences, uh, it, it's just incredibly important to think, look at it from all the different angles, right. For sure. And I think not just even in the creation of the, the actual tools and algorithms, but also in the job opportunity, I mean, we look at and see how I think San Francisco is a great example of where these tech companies came in and it benefited a lot of people, but it also then changed inequality for a lot of other people. And so when we look at it, this is the space to be in where the jobs are. You know, people talk about the jobs of the future and I'm like, no, it's not the future. It's here. It's right now, this is the present. And, and this is where, you know, a lot more people could be in this field if they just got the passcode of like, no, this word actually means this and put those pieces together and get rid of all of the buzzwords that existed it. And the feelings of making them feel like they aren't good enough or smart enough to be in a tech space. I can totally relate to that for the first two years that I worked in the data science field, uh, tangentially, I would often remind myself that I am a native English speaker <laugh>. Because. I wasn't quite sure I was following the language of these wonderfully trained academic PhDs all the time. Yeah. Um, but you, it is a lexicon and you get used to it just like you learn C plus plus or anything else. And you know, the other thing I'm resonating with you on is that making it accessible to people, coming to them where they are, are, and getting to them to their, where they wanna be. And that's actually why I chose to contribute to demystifying artificial intelligence for the enterprise, the book that's coming out later this year, because we need all of our business leaders. We need all of our, um, innovative thinkers and entrepreneurs to have access, to understand how to leverage this really tool, um, to accomplish ambitious goals. It's an accelerant. It's not a, a solution on its own, right. A hundred percent. I mean, I love that you called it a tool cuz really that's what it is. And you know, I've been a little bit saddened in the last couple years with the field of data science, just because it is starting to get more standardized, which is a good thing. But I think in some ways then we're leaving out some coming in and bringing in their knowledge and expertise and it's not data science, isn't a, a role, it's an overall tool that we use to analyze problems and critically think and come up with and something that we all can apply to our life, whether we're a business leader, an entrepreneur, like all those skills are applicable. I would love to hear a couple examples of different goals. You've applied to data science apply your data science expertise too, to accomplish, um, impact that you're looking forward. Do you mind sharing an example or two with us? So maybe people that are listening that maybe might be more on that business side can connect with and go, oh, that's how I can do it. Yeah. So I love to talk about like at a very personal level, how I use data science for my life. So I am a huge planner. People freak out because I have data on my myself for the past six years and half an hour increments of what I've done every day. So people are like, oh my goodness, you're a little psycho <laugh>. Researcher through and through, through. I it's in my blood, I can't get it out of me. But what I've learned from that process over time is so many people I used to think it was that I was a planner and that, you know, I kept all of this and I have these planning notebooks, but really what's actually more beneficial is what I do in the reflection process. So I don't note everything of, you know, what I'm going to do. What I'll do is at the beginning of the day, the day before I'll write in each of those half an hours, what I did that previous day. And so what that has allowed me to do is get past data, which is on myself, which then actually helps me predict the future of my trajectory, how I'm mapping towards my goals, where I'm likely to miss et cetera. And so when I look back at it, I was like, oh my goodness, I'm applying data science methodology to the planning and goal setting of my life where the first thing I realized is I have to have data on myself first to know what do all day, what do you do with your time? And then how do you start to optimize that time? And for me, like now the most important part is like how good and accurate is that data that I have on myself? You know, very basic fundamental principles of like your data architecture of like, where's, what's your quality of your data. Right? And then allows me to take some time to look at it, reflect on it and predict the future of where I'm going and what's gonna happen. In fact, I believe the first time we got a chance to meet in person, you were about to embark on a think week, right?<laugh> Do you mind sharing with that people how you recharge your batteries because you're obviously a driven, ambitious and Um, I bet a lot of people wouldn't believe that you take a full week to yourself. Yes. Um, this is something that it started back when I was in school, in my master's program. Um, there was the week between like Christmas and new year's that's, you know, know pretty much, no one's working. Some people call it dead week. And I was like, oh my goodness, this is the perfect time to reflect on the past year plan, the new year. And so I made it, you know, just a week where I didn't have any meetings, um, and I would try and go away somewhere or at least, you know, let people know in the household, like don't disturb me. My door's closed. I am working. It is a week of work. So don't think that you're taking like this vacation, but it's a week of work to reflect on yourself. And this is when I go through all the data, you know, I'll read like past years of my planners or, um, current, you know, what is my tr or it within the business going through and look at like all the financials, all the social media steps, everything that, you know, any data I can get my hands on and really use that time to just like soak it up. And you know, what I've heard from a lot of people is like, oh, I've taken some kind of think, you know, days or hours, but they're like, oh, whole week. And I was like, yes, you have to do it for a whole week because that really allows it to like go into the details and soak in. And that's when those times when maybe you're in the shower of that think week is when like the magic happens of the ideas of what to do next. And so those have gone from a yearly thing to now a quarterly thing. Um, we'll see if they become a monthly thing. I don't know if we'll be able to fit that, but right now a quarterly, um, quarterly think week is what works best for me. I love that. Cause I think we started in an economy in the fifties with like the thought of industrialization, where everybody is valued as an asset by their time mm-hmm <affirmative> and we're evolving into, uh, an information economy that really it's more, the creativity you bring, it's really more the impact you can drive and that's going to be the skills of the future. And I think you reflection of that change in culture. Um, and I've never met a woman who says I didn't trust my instincts and it turned out great. Have you met her yet? No, <laugh>. Never. Uh, and I also have never seen anybody that can be in the tyranny of extreme busyness and numbing out with work that is able to hear their own inner wisdom at the times that they need to right. Or create impact from that. I mean, you know, when we're so much in this, go go, go get things done. And we're not really looking up and navigating our ship to say, okay, where's the bigger picture. Less of like, what are the tasks to run this ship on a day to day basis, but where are we really headed? And I think if you're a leader of your organization, like I know you usually probably have some think time with your teams and brainstorming, but first and foremost, you need to get solid in your foundation. And that is what this is all about. And I think too often we bring a team together to have these sessions and we don't even know owe ourselves yet. And so for me, like the first week is think week for me. And then my team knows that I'm gonna come back with all these ideas and I'm gonna be reared up and ready to go. And so that then the time to bring other people together, but getting solid and your foundation is like first and foremost, most important. I love the, so you have, you're seeing all the emerging trends on the front lines of data science today. Do you mind sharing with us topics that you think we should all be paying a little bit more attention to? Yeah. So one of the things I see is just how easy it is to access the field. So if it, if, when I look back in the past, like 70 years in data science, you know, one, the tools we have used have changed the language we have used, but the knowledge that we have in the field, it is so much higher. So before, you know, your coding models from scratch, we were using R you know, now we have Python and there's all of these packages and libraries just out of the box. In addition to that, there's so many gooey tools as well. That's really just a click and drag and drop. And so where I'm seeing it going is focusing less on the tools and the technology, because that's really easily accessible and more on that business impact. Like how do we really, that's where the next frontier is? Like, how do we manage data science as an actual product? How do we make sure everyone in the organization has an understanding of this tool? How do we make sure that we're creating impact from it and not getting lost in the details of architecture diagrams who's gonna own, what and how are we gonna do it? Um, because it's so the it's, it's really not as complex as we make it to be. What is complex is the strategy of it, how we integrated into our business and, you know, the culture that we have within our business to use it. And just to build on an example of that, uh, when I was running my company minor guard, we had a summer intern who had come in to do marketing and communications for us. And so her background was, uh, she was a romance language major in college. Uh, she as a junior and she was coming into a tech company for the first time. And, um, our data scientist took her under her wing and taught her Python over the summer. And she realized it was pretty much as easy as learning French and Italian was, it's just another language. And she was able as her final project for school that year to use Python and Tableau to automate all of her homework for her senior year. Thanks. She was doing manual sentiment analysis of romance novel, or of novels in romance languages. And she was able to prove that with her predictive analytics she did in Tableau that it matched her manual, uh, assessment of it who knew.<Laugh> and that's exactly what we need. Right. We need people from these diverse backgrounds coming in and saying, oh, I can use it in this way. Like we have to stop thinking of like business and tech as two separate things. I mean, it's could crazy to me that these are even thought of as two separate things, cuz technology is a tool, we're all a part of the business. And so bringing those together, sharing knowledge and, and that cross training is so valuable. So say that you have accomplished so much. What are you most proud of personally, uh, in turn of what you've accomplished either in your career or in your teaching or in your nonprofit leadership, what comes to mind? Yeah, so I really love actually small data, which is kind of funny because in the realm that I work in, I work with with a lot of big data, right. <laugh> and you know, a lot of things, I think in our culture today, we are more attracted to the big numbers. Like we talk about how many chapters we have in women, in data and how many members and all of this, but really like what makes me super proud is the small data of a one person's individual stories. And I think what I'm most proud of in that regard is just individual stories of lives who have changed when you give them access to tools and technology that they can use to make their life better. And so <affirmative>, we've seen that in women, in data where, you know, women were, who were in abusive relationships and couldn't earn an income, were able to train themselves in this field, get a high paying job, leave that relationship and create a better life for them themselves and then their family. And so when I think about like, what makes me most proud, it's like, it's those stories. It's the, it's the little individual pieces of data that really are what makes me come alive and, and wake up in the morning and, and wanna do all of this over again. At the end of the day, we're all wired to connect mm-hmm <affirmative> and I think is a shared passion of myself and you and launch a consulting. That's where we really focus. Cuz at the end of the day, the tools are a means to an end it's how do you help the humans do what they need to do uniquely, uh, to get there. Right. Definitely. Yeah. So, um, how do you keep the key priorities that you have top of mind for decision makers? Sometimes AI ethics isn't top of mind, sometimes diversity and tech at top of mind, sometimes helping domestic violence survivors. Isn't what drives the, the bottom line of the P and L how do you help keep that impact in mind? Because you've been incredibly effective at it at places, uh, like Accenture at places like V S P healthcare. Uh, you have the led teams in house you've consulted externally and you've been incredibly impactful by speaking other people's languages in the way that they need to hear it. So can you share a little bit of that wisdom with us? Yes. I think first and foremost is just slow down. Like <laugh> I love, you know, one of BRNE Brown's principles of like getting of rid of that hustle culture and like the need to be busy. And I think so often in business, like we have this idea that if we don't move fast, right? Like it's a, it's a very startup kind of tech row culture, right. Of like move fast and break things. Right. And I'm like, well, I don't, I mean, that may happen, but like I don't really wanna break things. And so my whole goal and you know, how we've grown in women in data is talking about how do we scale sustainably? And I think a lot of that, if we keep these higher things, top of mind, it really comes down by first by taking a step back and slowing down. Right. Stop getting lost in the day to day of that, to do list and task and think of like overall objectives of like, what is the company impact that we're trying to make? Are we achieving that? And how do we make sure that like we're doing it in an ethical and sustainable way that takes deep thought these are not questions. If they were questions we all had answers for, we'd probably already have automated at this point, but we haven't. And why we haven't is cuz it takes conscious deep thought and collaboration. And to do that, you have to slow down and don't ever think that you're gonna miss anything or lose anything from that. I think all of us have a little bit too much FOMO in our life, but it, but I oftentimes look at the Sequoia trees and, and look and see like, these are things that aren't fast and growing, but they've lasted a lifetime and they've lasted a lifetime because they're a part of a net work that bonds together and they take each one day at a time. And I think sometimes we look too much for small term gains versus long term rewards. And if we slow down, it allows us to get rooted in that, which is gonna be sustainable for the long term. Boy, it sounds like that time growing up immersed in nature <laugh> and Has really benefited you in terms of wisdom. Well, beyond your years, it, it is, uh, really inspiring to hear that how you're seeing things. It took me a lot longer of hitting my head against the wall, uh, to figure some of that stuff out. And so it gives me a lot of hope for our future with emerging leaders like you, Sadie that are, that are taking the, the charge forward. Um, I also wanted to share just a little nugget of knowledge I'm not gonna cite it properly, but I know somebody at pivotal venture shared it with me. So it was research informed. Is that the most common method to get into a job in the data science field, if you don't graduate from an Ivy league school is barista, apple tech support, Then major tech company with software engineer, big, big job. So I wanna demystify some of that process for people that maybe don't have the academic record. You mentioned a C T was or test, uh, or maybe don't have the financial support to not be SU sustaining themselves as they're progressing through their career journey. Um, you know, the barista skills, get you interacting with customers and getting you really comfortable with all the different ways you have to be in the world. And then apple support really values all those great customer service skills and you chew the technical chops and you can self learn. Uh, as you mentioned, uh, you're on Coursera with data science classes. Do you mind demystifying a little bit of that piece of things of if people wanted to have free education in this space that can show that they're capable and worth taking a chance on how Coursera or things like that can be a tool for them? Yeah. So I actually, before I started my master's program, I started courses on Coursera just to see if I even liked it. Um, and that really was what led me to get into the field because I soon found I really enjoyed it. And that's what led me to go get my masters today. I really don't feel like, you know, I think if I was doing it all again, I wouldn't probably get my masters. There's so much information out there now it was, you know, a different time, six, seven years ago. And so I see so many people who are able to take some classes part-time they may switch up their job a little bit. So they have a little bit more free time. I think that's where the barista works well is like, Hey, I'm able to pay my bills, but it gives me some more free time, which allows me to study and allows me to network. Um, but it is possible. And I think a lot of it though comes down to is really like the community that you're a part of. So like I've only ever applied to one job, but that was the first job I had outta college. And then from there, like my network has carried me and I think that's like a main thing I want people to understand is like, it comes through your network and not only through your network of getting the job, but also knowing what to learn. Okay, you've taken this class, you liked it. There's a lot of information out there. So having people you can bounce ideas off of and collaborate with is gonna make sure that you find that success in getting the job you want. Okay. And how, how can people support your work? Maybe if they're, uh, not interested in pursuing this field, but interested in supporting people like you and people that you're lifting up through your community. Do you have any suggestions on ways people can be supportive? Maybe you, you wanna become a mentor. We're always looking for mentors. That's a great way to give back. Um, another option is just, you know, sharing that women and data is available and sharing it with your friends and family who it may be useful for. Um, you can also participate through, you know, different corporate sponsorships and partnerships, um, and then also becoming a member. So definitely a lot of ways to get involved. Wonderful. And if I'm not mistaken, you host your own podcast now, correct? Yes. Um, so we have a, a podcast called the data bites podcast. We share stories of people in the industry. Um, and then just also career tips and career advice. Um, for those who are looking to get in the field, they're looking to move into management in the field. Yeah. So women in data, you can find firstname.lastname@example.org, and then you can with me on any social media platform or on my website, just at Sadie St. Lawrence, um, yeah. Happy to connect with anybody. Well, thank you so much for being here today, Sadie. It was a pleasure speaking with you and it always is. And um, we look forward to continuing the conversation in the future. Sounds good. Thanks so much for having me. Hey everyone. Thanks for listening to the navigating forward podcast. We'd love to hear from you at a crossroads of uncertainty and opportunity. How do you navigate quality? We'll see you next time.