Tell us about your background.
I grew up in Los Alamos, New Mexico. Though it’s a small city of about 20,000 people, it has a great education system since it is home to a National Lab. My dad was a computer scientist (he helped invent one of the first mass scale storage systems) and my mom was an artist, who encouraged my artistic side, especially in music (piano, flute, and piccolo).
I was fortunate to have my dad supporting me in math as a child, and when I went to UC San Diego he strongly encouraged me to major in something STEM-related. I ultimately settled on Applied Math and Scientific Programming, which gave me a good blend of numerical methods, statistics, and programming in a number of languages. While in college I worked at the San Diego Supercomputer Center, and during summers I worked at the Lab in Los Alamos, programming graphical simulations for the Strategic Defense Initiative. I have tremendous gratitude that relative to many women of my generation, I had lots of support and encouragement to pursue a STEM career, and that the blend of “art” and “science” in my upbringing was a great foundation for telling stories with data.
How did you get interested in data science?
After undergrad I considered a variety of options but ultimately pursued a Biostatistics degree at University of Michigan. I loved the idea of using math for the benefit of people. I started my career at Procter & Gamble, working on pharmacokinetic studies for clinical trials. About three years in, I had the opportunity to work on a project to build a Trade Promotion Optimization system, which would help brand managers figure out how much they should spend promoting Tide at Walmart or Pampers at Target. Underlying this system were models of consumer demand that took into account price and promotion elasticity, seasonality, trends, and the cannibalization that occurs when prices change between substitutable products. I found this super interesting, partly because this type of econometric modeling was evolving quickly, and I also really enjoyed collaborating with the business people at P&G to understand the questions they wanted to answer.
I ended up leaving to join a small startup company called DemandTec, which was the original cloud-based price optimization provider. I was the first statistician hired and started building up a team, which was hard in the early 2000’s because data science wasn’t really a thing yet. But over the next 12 years, we grew the company and worked with big global retailers like Walmart, Target, Sainsbury’s and Carrefour, and were ultimately acquired by IBM. It was an amazing experience, both because I was part of the executive management team and because what we could do with data and analytics evolved so much in that time period. We started out modeling aggregate data to do in-store pricing and ended up leveraging transaction-level data to set prices dynamically for both stores and websites.
What are you currently working on?
I joined Facebook in early 2018 and it has been another incredible journey. I started on the Product Analytics side of the house in Ads, and my teams supported a wide variety of initiatives, including the Shops product that launched during the pandemic in spring of 2020. But I realized that I missed working closely with business people. My superpower is really building bridges between technical people and their cross-functional partners, and ensuring that the insights that data scientists provide are useful to the business. So last fall I moved over to supporting data science in the Global Marketing Solutions team, which focuses on improving and accelerating the value that businesses get from Facebook’s products, like Ads, Pages, Shops and Messaging. We tackle a wide range of problems. For example, we are responsible for measuring the effectiveness of our sales and marketing programs, and use machine learning to match businesses with the best support and personalized guidance based on what we know about them.
How did you first discover WiDS?
I found out about WiDS when I was working at IBM. One of my colleagues suggested I attend the second annual conference because I was one of the only senior technical people who was also a data scientist. I hadn’t really reflected on the fact that throughout my career, most of the academics and colleagues that I worked with were men, and at the conferences I attended, virtually all of the speakers were men. But attending the WiDS conference felt both inspiring and comfortable. The attendees were incredibly accomplished but very accessible and genuine, and everyone was interested in networking and understanding what other people were working on without a particular agenda.
Have you been involved with WiDS since that first experience?
When I joined Facebook, I became involved in our internal “Women in Analytics” organization, which seeks to support women with career resources and community, and also ensure that their contributions are appropriately recognized. I became aware of an opportunity for Facebook to support WiDS as a corporate sponsor and enthusiastically championed it. Starting in 2019 we were able to sponsor micro-scholarships that allowed WiDS ambassadors from around the world to come to Stanford and learn best practices to take back to their local chapters. I’m hoping we can increase Facebook’s involvement in the future as conference speakers and through other types of participation, like the Datathon and the Educational Outreach program.
How has WiDS made an impact on your life and/or work?
WiDS has helped me build my professional network, and we were fortunate to sponsor recruiting events in both the US and Europe/Middle East as part of the 2021 virtual conference. But I would say the biggest impact is inspiration, both from hearing data scientists speak on a wide range of topics such as applications of machine learning, ethics, and economic opportunity, and in seeing great role models for engaging and authentic presentation of analysis.
What comes next for you? And what are your hopes for women in data science in the future?
I have just taken on expanded scope in Facebook GMS, and I’m excited to continue growing our team with talented data scientists. One very interesting initiative that I’m involved in is helping people understand how personalized ads are important to small businesses. I think we have a long way to go in educating people on what Facebook does and doesn’t do with their data, and on the value that we provide by helping businesses inexpensively reach customers who will love their products and services.
In terms of hopes for women in data science: I hope that we can make the case to young girls that there are incredibly interesting opportunities in data and analytics and AI/ML, and find ways to support them through every step of their education and careers.
How has participating in WiDS impacted you? Send your #WiDStory to: email@example.com.