How Lyft Changed My Life

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I just thought that ultimately that was where the more interesting and applied science was. The thing that turned me off from the financial industry is that the data is constantly fighting you. And so I just kind of morally got turned off from the industry. Like we really were, it is a little bit of a cliche but we really were just like dealing with very rich people and trying to make them more money. From a distance it looked cool because it looked like a fun game. But once you got in there you're like, okay, this is actually pretty unfulfilling. So I just moved over into tech because it seemed more appealing.

What causes us to keep returning to the topic of causal inference on this show? DAG if we know! Whether or not you're familiar with directed acyclic graphs (or… DAGs) in the context of causal inference, this episode is likely for you! DJ Rich, a data scientist at Lyft, joined us to discuss causality — why it matters, why it's tricky, and what happens when you tackle causally modelling the complexity of a large-scale, two-sided market! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.