Lyft Data Science is hosting a cross-company meet up event! This event will include 3 talks by 3 data scientists from Amazon, Lyft and Square, and will include networking sessions before & after.
Food and refreshments will be provided (with vegan/vegetarian options). All attendees are welcome, including roles in ML / data science / analytics or adjacent roles!
Sr. Applied Science Manager - Amazon
Talk: Finding Your Next Favorite “Song”
— the Music Recommender System Analogy for Career Growth
Abstract: The tech world is going through a period of rapid growth and change... as always. How do *you* navigate the changes and grow? Having worked in the music recommendation field for 10+ years, I'll draw on the recommendation models to explore the conceptual similarities between building science-driven products and building a scientist's career. I'll share anecdotes and my learnings, and call upon you to be your own best recommender.
Data Science Manager - Lyft
Talk: Applications of Adaptive Experimentation at Lyft
Abstract: In a world of fast-paced development, it is important to have a diverse experimentation platform that not only accommodates standard practices such as A/B testing but also provides new solutions using machine learning techniques including parameter tuning and multi-arm bandits. At Lyft, we take these various models and apply them across all our lines of business, from design decisions on the app to dispatching drivers. By utilizing these always-on experiments, teams can adapt their systems to dynamic market conditions, allowing for faster impact measurement and decision-making. Our session aims to inspire experimenters at other companies to creatively use adaptive experimentation to continuously explore a discrete parameter space.
Data Science Tech Lead / Staff Data Scientist - Square
Talk: Overcoming Challenges and Pitfalls of A/B Testing
Abstract: This talk goes into depth about the most common challenges and pitfalls that I have experienced throughout my career and how to avoid making the most common mistakes. After the talk, you will know what to do when someone asks you to analyse an experiment you haven't designed, how to deal with partners asking for 'directional data' and how to work successfully with engineering to ensure each test is set up correctly.
Bio: Dr. Tao Ye is a Sr. Applied Science Manager at Amazon, where she leads a team of scientists and engineers at Amazon Music to work on music conversations, conversational recommendations, and LLM applications. Prior to Amazon, she was a founding member of the Pandora science team and Director of science. In the larger research community, she co-founded Women in Recsys, has served on the steering committee of Recsys since 2018, and has served as industry co-chair for 2018 Recsys, 2022 SIGIR, 2022 CIKM and 2024 SIGIR.
Talk: Finding Your Next Favorite “Song”
— the Music Recommender System Analogy for Career Growth
Bio: Anahita leads the Experimentation team at Lyft, focusing on providing an easy to use and extensible platform for Lyft to ensure changes to Lyft's products are made confidently with measurable benefits. In the past, she has held roles in the pricing and matching in supply chain, climate and retail domains.
Talk: Applications of Adaptive Experimentation at Lyft
Bio: Kasia is a Data Science Tech Lead at Square, where she collaborates with her team to drive data-informed decision-making. Her expertise spans various domains, including identity verification, sales analytics, ecommerce, and infrastructure. Prior to her current role, she gained valuable experience working at Medium and FiveStars. In these positions, Kasia conducted numerous A/B tests to facilitate informed product decisions and actively contributed to company-wide A/B testing initiatives. In her free time, she indulges in her passions for travel, scuba diving, and reading.
Talk: Overcoming Challenges and Pitfalls of A/B Testing
