Bio: Gina Longo is the Senior Manager of Content and Personalization Analytics at SiriusXM/Pandora, overseeing all of the analytic insights used to drive decisions around personalization, content recommendations, and Creator promotion. She also co-leads the Women in Data organization at SiriusXM, where we provide a safe community for our scientists and analysts, and is an active mentor as well. Outside of work, she loves travel, is slowly sewing her own wardrobe, and can often be found spoiling her cat.

Talk: Demonstrating Leadership through AB Testing

Abstract: AB Testing is a staple of many data organizations, and a great opportunity for analysts and scientists to work cross-functionally and improve their product using data. In this talk, we will not only cover the basics of AB Testing, but also how at each stage we can elevate our work and lead through our analysis.

Bio: In her role as the only Data Scientist in the User Research organization at IBM, she supports 80+ user researchers and designers. She has delivered 45+ talks and workshops at conferences globally including PyCon US. She advocates for women in tech through her roles with Women in Data Science, Society of Women Engineers, DevNetwork Advisory Board, and was recently named as one of Women Who Code's 100 Technologists to Watch.

 
Talk: Data Science Beasts and Where To Find Them

Abstract: Join Grishma as she takes us on an adventure to find the beasts i.e. the different ways Data Science projects can fail. Some of these will be ones she has encountered in her experience as a Data Scientist.

Bio: Jinshu leads the Pre-request Science team with a diverse portfolio spanning across machine learning, causal inference, user experience optimizations, and strategic deep dives. The team drives technical solutions across Partnerships, Search, Assistive and Recommendations with the goal to make it seamless for users to find the best options to travel with Lyft. Jinshu joined Lyft in 2019 and has worked on several problems and product areas across app redesign, scheduled rides, recommendations, and ETA reliability through the past 4 years. Prior to Lyft, she was a Data Scientist in financial services and consulting companies.

Talk: Recommendation System at Lyft

Abstract: In this talk, we’ll give an overview of the recommendation system and how we use it to customize the app experiences and manage the marketplace.

Bio: Negar grew up in a small town in Iran and from the beginning was obsessed with Math. After the entering exam for college, she chose to study industrial engineering because, in her head, it was a combination of math and business. From the beginning of her undergrad, Negar pursued operation research courses and did her final project in this space. Then, continued her master degree in the same area and no surprise her PhD as well. Negar moved to the US back in 2011 to pursue her PhD from University of Washington. In the last 10 years, she has been working in the field of data science for companies like Apple , McKinsey and now Lyft.

Bio: Paula was drawn to operations research due to its efficiency and elegance. Her PhD focused on how to better run the optimization of near-term energy markets. From there, she worked on power generation bidding and scheduling. She now uses her knowledge of locational pricing to work on balancing the supply and demand of the rideshare marketplace through adaptive pricing, involving optimization, machine learning, and analysis.