Doordash Data Scientist Interview Guide

DoorDash Data Scientist interviews in the supplied accounts most clearly emphasize SQL and structured product reasoning. One candidate received invitations directly to a case study and technical interview after completing a questionnaire. The technical portion contained four complex SQL questions in 30 minutes, mainly involving several CTEs. The candidate was not allowed to execute and debug the code, making correct syntax and a workable approach especially important under the time limit.

The reported cases were grounded in delivery operations. One candidate was asked how to introduce bikes for dashing. Several closely related accounts describe diagnosing a decline in successful deliveries in Los Angeles by defining the metric, examining the delivery funnel, and separating possible supply, demand, restaurant, routing, geographic, and external causes. Build the diagnosis before proposing the fix: clarify what changed, segment the problem, prioritize hypotheses, and explain what evidence would distinguish them.

A candidate who advanced beyond the initial technical and case rounds reported an onsite loop containing four product case studies and a behavioral conversation with a product manager. That account included root-cause analysis and an A/B testing question about ads. The candidate felt the questions appeared simple but required crisp, deliberate reasoning rather than unnecessary technical complexity.

Practice writing multi-CTE SQL without depending on execution, and rehearse product cases aloud so that definitions, hypotheses, evidence, and recommendations follow a clear sequence. The supplied reports support paths ranging from two initial interviews to seven interviews including the final loop, but they do not establish a typical end-to-end duration.