Doordash Data Scientist Interview Guide

Candidates describe the DoorDash Data Scientist interview as a short first stage followed by a case-heavy loop. The first stage pairs SQL with a product case. Reports describe it as about an hour in total, or as two back-to-back 30-minute halves. One candidate worked through four related SQL questions on the same tables that got progressively harder, so watch edge cases and pace yourself on the later questions. Another describes a rolling-window aggregation and a day-7 retention query where the signup date had to be inferred from activity data. That candidate was also told to tighten up on CTEs and window functions. Calling out your assumptions explicitly is worth practicing.

The case half is where most reports focus. A recurring prompt is diagnosing a drop, such as fewer successful deliveries in Los Angeles or orders falling in a specific location. Break the problem into supply, demand and restaurant-side causes, and prioritize hypotheses before proposing solutions. Separate short-term fixes from longer-term ones. Another reported prompt asks you to define success metrics for each side of the three-sided marketplace for a fast-delivery feature and plan its rollout. In that case the candidate was pushed on A/B testing in depth, including sample sizing and multi-arm bandits.

Candidates who reached the later loop describe three or four product case interviews plus a behavioral conversation with a product manager or product partner. One candidate found the cases overlapped. Another said the questions were simpler than expected and rewarded clarity of thought over technical complexity. Outcomes vary: one candidate cleared the technical screen but stalled at team matching, and another did not receive an offer. Practice structuring answers out loud, writing down your structure, naming metrics for each side of the marketplace, and stating tradeoffs explicitly.