 Live from Stanford University, it's theCUBE. Covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE Media. Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the fifth annual WIDS, Women in Data Science Conference. Joining us today is Lillian Carrasquillo, who is the insights manager at Spotify. Lillian, welcome to theCUBE. Yeah, thank you, Sonia, for having me. So tell us a little bit about your role at Spotify. Yeah, so I'm actually one of a few insights managers in the personalization team, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So from your daily mix, to discover weekly, to your year-end rap stories, to your experience on home and the search results. That's awesome. Can you tell us a little bit more about the personalization team? Yeah, so we actually have a variety of different product areas that come together to form the personalization mission, which is mission is like the term that we use for a big department at Spotify. And we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app. Great, and so you're gonna be on the career panel today. How do you feel about that? I'm really excited, yeah. Yeah, the WID team has done a great job of bringing together diverse, it's an overused term, sometimes there are a very diverse group of people with lots of different types of experiences, which I think is core to how I think about data science. It's a wide definition. And so I think it's great to show younger and mid-career women all of the different career paths that we can all take. And what advice would you give to women who are coming out of college right now about data science? Yeah, so my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientist or a team that says data scientist. You can follow your interests and do your data science, use your data science skills in ways that might require a lot of collaboration or mixed methods or work within a team where there are different types of expertise coming together to work on problems. And speaking of mixed methods, Insights is a team that's a mixed methods research group. So tell us more about that. Yeah, so I personally manage a data scientist and a user researcher and the three of us collaborate highly together across our disciplines. We also collaborate across research science, the research science team, right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative and the idea is to understand the problem space deeply together, be able to understand what is it that we're trying to even just form in our head is like the need that a user or end user human has and bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, a product owner can really think through and understand the current space and like the product opportunities. And to understand that user insight, do you use A.B. testing? We use a lot of A.B. testing. So that's core to how we think about our users at Spotify. So we use a lot of A.B. testing, we do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think A.B. testing, there's so much to learn about best practices there and when you're talking about a team that does foundational data and foundational features, you also have to think about unintended or second order effects of algorithmic A.B. test. So it's been just like a huge area of learning and a huge area of just like very interesting outcomes and like every test that we run, we learn a lot about not just the individual thing we're testing, but just the process overall. And what are some features of Spotify that customers really love? Anything you can dish on? Anything that's like we know you. So daily mix, people absolutely love. Every time that I make a new friend and I tell them what they work on, they're like, I was just listening to my daily mix this morning. Discover weekly for people who really want to stay open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have. Just the nostalgia that people have even just for the last year, but in 2019, we were actually able to do 10 years. And that amount of nostalgia just like went through the roof. Like people were just like, oh my goodness, you captured the time that I broke up with that person five years ago or just like, oh, when I discovered that I love Taylor Swift, even though I didn't think I liked her or something like that, you know. Are there any surprises or interesting stories that you have about interesting user experiences? Yeah, I mean, I can give you an example for my experience. So recently, a few months ago, I was scrolling through my home feed and I noticed that one of the highly rated things for me was women in country. And I was like, oh, that's kind of weird. I don't consider myself a country fan, right? And I was like having this moment where I went through this path of, wait, that's weird. Why would this recommend, why would the home screen recommend women in country country music to me? And then when I clicked through it, it would show you a little bit of information about it. It's because it had, you know, Dolly Parton, it had Margot Price and it had the High Women and those were all artists that I'd been listening to a lot but I just had not formed an identity as a country music. And then I clicked through it and was like, oh, this is a great playlist and I listened to it. And it got me to the point where I was realizing I really actually do like country music when the stories are centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the songwriting of these other country acts. That's so cool that you discovered that. So you have a degree in industrial mathematics and you went to a liberal arts college on purpose because you wanted to try out different classes. So how has that diversity of education really helped you in your career? Yeah, so my undergrad is from Smith College which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he considers studying math not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what the problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I have an undeclared major in Latin American studies and I studied like neuroscience and quantum physics for non-experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school. And I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class. I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. And would you recommend having this kind of like diverse education to young women now who are looking into going to society? I absolutely would. I mean, I think, you know, there's a, some people believe that instead of thinking about STEAM, we should be talking, instead of thinking about STEM rather, we should be talking about STEAM, which adds the arts education in there and liberal arts is one of them. And I think that now in these conversations that we have about biases in data and in ML and in AI and understanding fairness and accountability, accountability, sorry, it's a hard word apparently. I think that a strong cross-disciplinary, collaborative and even on an individual level cross-disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects we're having based on the decisions of parameters for a model, you know, in a local sense where optimizing and doing a great job but what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. And speaking about bias earlier, we heard that diversity is great because it brings out new perspectives. It also helps to reduce that unfair bias. So how at Spotify have you managed or has Spotify managed to create a more diverse team? Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there and there's a great team that like thinks about that exclusively and they're really pushing all of us as managers, as ICs, as leaders to really think about the decisions and the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environment because it's not just about diversity, it's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out their places in technology, whether it be because they come from a different culture or they might be gender non-binary, they might be women who feel like there isn't a place for them. And it's really about, you know, the things that I think about is because you're different, your voice is needed even more. You know, and like your voice matters and we need to figure out and I always ask, how can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence, you know, more than some other folks. How can I help other people acquire that as well? Lillian, thank you so much for your insight. Thank you for being on theCUBE. Yeah, thank you. I'm your host, Sonia Tagare. Thank you for watching and stay tuned for more.