Dear Analyst #119: Developing the holy “grail” model at Lyft, user journeys, and hidden analytics with Sean Taylor

Future Dear Analyst episodes will get more sporadic since, well, life gets in the way. Unfortunately curiosity (in most cases) doesn’t pay the bills. Nevertheless, when I come across an idea or person that I think is worth sharing/learning more about, I’ll try my best to post. In this episode, I interview the Chief Scientist of a data startup who did his PhD at Stern NYU and was on track go becoming a professor. Then he got an internship at Facebook and everything changed. The speed of learning at a tech company outpaced what the academic was used to at university. Over the years, Sean Taylor has worked with and spoken to hundreds of data analysts and statisticians. We’ll dive into his data science work at Lyft, his notion of “hidden analytics,” and why he’s obsessed with user journeys in modern applications. Modeling the Lyft marketplace and creating the GRAIL model Sean worked at Facebook for 5 years as a research scientist and worked on general data problems. Eventually he joined the revenue operations science team at Lyft. His team’s goal was to help grow the marketplace of riders and drives on the platform. One of the most important aspects of the marketplace is the forecast. As Lyft runs promotions and enters new cities, how do you ensure there are enough drivers for the riders and vice versa?