Recruiter call to just check if you get how a two sided marketplace actually works and if you align with their core values. After that was a 45 minute live screen that was mostly advanced SQL window functions and some basic metric diagnostic questions. The onsite was a 5 round loop covering product sense, stats and experimentation, applied modeling, data processing, and a behavioral bar raiser. The stats and experimentation round is the real filter i think. You cannot just suggest a standard A/B test for a new feature. They really push you on network effects and driver cannibalization, so you have to know switchback experiments and synthetic controls inside out. Product sense was basically a deep dive into root cause analysis, like being asked to figure out why rider cancellations suddenly spiked in a specific city and walking through the exact metrics you would pull. The modeling round was less about writing math on a whiteboard and more about how you handle imbalanced data and pick the right tradeoffs for putting things in production. The bar raiser chat is pretty intense. They will dig deep into your past projects to see if you actually drove the business impact or just wrote the queries, and they care a lot about how you push back on product managers. For prep, do not just grind leetcode database questions. Practice structuring ambiguous product metrics and read up on their engineering blog. Doing a mock on prepfully with an Uber DS helped me to catch my blind spots with the switchback experiment stuff and get a reality check before the actual loop. Tough process overall but really engaging.

It was with a referral from a friend. Basic recruiter questions. Call for tech screening - Python I was hoping for Pandas type questions which I use in my day job. They asked about creating a function to get cumulative sum