Doordash Data Scientist Interview Guide

DoorDash Data Scientist interviews described here commonly begin with a technical screen combining SQL and a product or business case. One candidate described a 60-minute interview split evenly between SQL and a case study, while another reported four SQL questions of increasing difficulty followed by a launch case. Window functions were explicitly reported. Practice completing a connected SQL set under time pressure, explaining your reasoning and checking edge cases as the questions become harder.

The case material is tied closely to delivery-marketplace problems. Candidates described diagnosing a drop in successful deliveries or orders, evaluating the launch of a bicycle feature for Dashers, and reasoning about an advertising experiment. A strong response starts by defining the metric, market, and time period. It should then organize hypotheses across driver supply, customer demand, restaurant operations, routing, and external conditions before prioritizing analysis and possible interventions. For feature questions, define success measures and explain how you would evaluate whether the launch caused the observed result.

Candidates who advanced described a later virtual loop with four product cases and a behavioral conversation with a product manager. Another report described panel interviews involving a product partner and three cases, so the exact composition varies. Prepare concise cross-functional examples alongside SQL and case work, and practice presenting a clear structure even when the prompt initially appears simple. No candidate supplied an explicit end-to-end duration.