>>Welcome everyone. This is Stephanie Chan with the cube, but this conversation is part of Ws 2022 coverage. Today, we'll be speaking with Lila cor managing director at Google. Welcome to show Lila. >>Thank you so much. It's great to be here. >>So how did you come to be a data science leader? >>Yeah. Thank you. Um, you know, let me tell you how I came to be a data science leader, and also just thank you again to, uh, WIDS for having me here, this mission to support those in university or aspiring to be data scientists and those who are in the fee. It's just so important and inspiring to me. So it's been great to see this interest in WIDS and data science from young people all across the globe. So just thanks for having me here, uh, let me tell you how I came to be a data science leader. It really starts with identifying what you're passionate about and what you enjoy and what you're good at really passionate about using data to solve problems. I enjoy problem solving with data and analytics and following these passions led me to take classes in math and economics and econometrics. >>And I also took classes in political science and public policy have a diverse background. Um, and I think that having diverse backgrounds around the table are critical and an asset, um, but that those, uh, courses and that, uh, getting a, that undergrad and a master's started, I started my career as an economist at the us treasury department. And then I moved into technology over 20 years ago. I joined a startup in early 2000 and I've been a big tech companies like Google as well as at startups. And I really realized early on, uh, in my career, how much I enjoy data driven decision making. And I understood how powerful of a role data plays in making informed business decisions. There's just so much uncertainty in the problem that we're trying to address. There's a lot of ambiguity. Um, and data science is just absolutely critical to helping think through making those decisions and uncertainty. >>Another passion of mine is asking questions. My teams will tell you, I like asking a lot of questions and that is key. Uh, when you're a data scientist and we, you lead data science team, who's asking a lot of really good questions and impact. That's also super important to me. The best feeling is the impact you can make on a company with data. Finally, I'm also passionate about managing people and team leadership and applying data to solve cross-functional problems across so many different functions. I've been able to work product and marketing and strategy and operations. And by following these passions, I've gravitated towards managing more quantitative and analytical teams that are also passionate about using data and analytics to grow businesses. And when you work at Google, you're surrounded by this culture of innovation and a culture that's focused on translating data into value. So that's how I ended up becoming a data science leader. Um, you know, my advice to everyone is just like, uh, you know, to is stay curious, think about your passions, what you enjoy doing and the type of problems that you enjoy solving and that, that, and think about the kind of impact that you are looking to drive in this world. For me, that's led to leading data science teams and other broader teams, um, and I had to be open and flexible to where that might take take you, uh, it, it might be different from what you envisioned front >>And speaking of your teams, can you tell us about your teams and the work they do? >>Absolutely. Um, so I'm currently the managing director of Google's technical professional services and marketing data science teams for a group called the global clients and agency solutions. And we help the world's largest brands. In the digital age. We work directly with some of the world's most sophisticated, uh, chief marketing officers and marketing organizations in the world. So I'm honored to lead an organization that develops advanced engineering and data science solutions for Google's largest customers and largest advertisers. My team include customer solution engineers. They include engagement managers, they include technical specialists, and they also include, uh, data scientists. There's, you know, PhD, statisticians, economists, former consultants with deep experience in data science, machine learning and marketing analytics, uh, in my teams and my data teams, they find insights that change the business, uh, in the future. They're, they're amazing. And they do work. That's really groundbreaking. Actually. Can I tell you about some of the superpowers of the data scientists on my teams? >>Of course, I would love to hear it. >>Yeah, well, um, our marketing data science teams, they help measure optimize marketing, uh, a return on investment for Google's largest global clients. So one superpower is their customer obsessed. Um, they, we sit down at the C level table and with our customers, we ask a lot of questions so that we can understand the customer's business objective and how data can help them think through the various options they have. Another superpower is my teams are really good at asking really important questions. You know, you need to really have that back and forth to understand what your customer, what your exec, what they're trying to achieve. Um, and then they build cutting edge complex models that address our customers, key business questions that translates into things like marketing analytics, marketing, mix modeling, statistical modeling, machine learning, uh, you know, digital attribution, predictive analytics, my teams, they create rigorous experiments to help deliver the best possible solutions to our customers. >>Um, another superpower is helping to make better decisions in uncertainty. This is key data. Scientists are so good at this and my teams help these big cutting edge, you know, C level execs and CMOs and marketing organizations all around the world. Um, they help them find better ways to achieve peak marketing ROI. I've been a VP of marketing, um, you know, in startups and throughout my career, I know how hard it is and how important it, it is to grow your business with marketing, um, and really impact, uh, a business and all of their customers. So I'm really proud of the groundbreaking work that my teams are doing to help the world's biggest brands grow, uh, in the digital age, this just one of the types of careers and data science. Uh, my teams were in the, a business organization that works with marketers and advertisers, but I've also been able to lead teams that work with Google's product and engineering leadership to improve our products as well with data science. And there are data science teams in so many different parts of Google that are working on really complex, important challenges, whether it's in our global business organization, whether it's in Google cloud or Google product, YouTube, Google health, I mean, Google health, you know, has done some amazing things using artificial intelligence to prevent blindness. So that's a little bit about my teams and the work that they do >>And what career skills and experiences are most important to you as a data science leader at Google. >>Yeah. Um, thank you. Like I mentioned, we have such a great culture here at Google, a culture of innovation, um, a culture of really trying to solve complex and hard problems, important problems. And these problems have a lot of ambiguity. A lot of uncertainty, there's not always a, a clear right answer. This is where data science can just have such a huge impact. So of course, there's the strong foundation that we look for in the core data, science skills, stats, econometrics, and math, but some of the other skills that are so important, I would say being clear on the problem that you're trying to solve, uh, and focusing on what matters most, this is so important when you're faced with complex ambiguous, multifaceted problems to not get lost in the details or lose focus, asking those really important and, uh, questions and really trying to understand the problem that your customer or your exec is trying to solve. >>So ask, uh, being clear on the problem that you're trying to solve and asking really good questions. That's a, a, a key skill, um, that I think is very important at Google. Another one is the importance of storytelling. I mean, without a good narrative, it can be hard to move from data to insight. And when you're faced with lots of data, you know, being able to distill that complex data into a meaningful and coherent and impactful story. So those strong narrative and communication skills, they're critical, they're critical to ensure that your customer or your exec or your audience, here's the insight that these types of skills, data science skills can help uncover. And I've just add one more, which is there's a skill around thriving and uncertainty and thriving and ambiguity. You know, there's, you'll, it's just inevitable. Um, you've got to, you're gonna hit roadblocks. There's gonna be setbacks. There's a lot of complexity. So being able to be flexible, being able to pivot, being a leader, a role model about how to bounce back, helping others to do so. That's a really critical skill because a lot of the work that we're doing, uh, at Google and specifically data science, they're here to help people think through uncertainty. So those are some of the, um, the skills and experiences that I think are most important to me as a data science leader at Google >>And throughout your career, what is the best piece of advice you have received? >>Uh, thank you for that question. Um, I've received a lot of really great advice, but if I were to pick one for this group, it would be never underestimate the power of showing the world. What's possible. Ruth Perra said that, um, I heard her say that once, and she's the CFO at Google, and it really resonates with me. It's a great reminder of how powerful role models are. They provide us with inspiration and a vision for who we can aspire to be. They help us dream bigger dreams for ourselves. I know I've benefited so much from role models all throughout my life and career who show, show me what's possible. And that idea that you are showing someone else what's possible that they may not have envisioned for themselves. Well, that's super inspiring, motivating to me. So don't underestimate that power that you are providing visual proof, uh, for others about being leaders in data science or to technology. >>And I'd, you know, when I reflect on that advice, I also realize you don't need to have a big title to do this, to show the world what's possible. I have two daughters, my 10 year old daughter. She's inspiring people all the time, including me. Uh, my eight year old daughter is a role model for others in the community, including me. Um, I see courage and inspiration all around me every single day from my team members. Like I, uh, mentioned from friends, from colleagues, from community members. There, there there's so many important firsts that they're role modeling, whether they're first in their family to go to college or the first to pursue data science or just so many other important firsts. So I would say never underestimate the power of showing the world. What's possible. That's a great piece of advice I've received. >>And this, my last question for you, what, what is one thing that you want all of the aspiring data scientists or women in the field who are listening to this interview to take away? >>Yeah. I would want them to take away that your voice matters. You belong at this table for everyone who is listening in the audience. You know, those of you in universe are aspiring to be data scientists. Those in the field, the world needs you. We need you to be data scientists. We need your voice and your insights at the table to address the biggest challenges in business and technology in the environment, in health, in society, you belong, you belong in data science, you belong at that sea sweet table. You belong here, you belong in your voice matters. >>Well, thank you so much for teaching us more about science and all your advice. >>It's a pleasure. Thank you again for having me. I really appreciate it. >>I'm Stephanie Chan with de cube. We'll see you next time.