Motivation for DoorDash and Data Science Role Questions
Topic covers motivation for applying to DoorDash and specifically to a Data Science role, including alignment with DoorDash's mission, product strategy, and data-driven decision making, as well as demonstrating cultural fit and value you bring to the team.
HardTechnical
0 practiced
DoorDash's mission to empower local economies raises fairness and ethical considerations. Identify two concrete fairness or ethical risks that could emerge from merchant or driver recommendation systems, and for each risk propose practical checks, monitoring metrics, and remediation steps a data scientist should implement.
MediumTechnical
0 practiced
Many DoorDash initiatives require rapid iteration under uncertainty. Give an example of a fast experiment or prototype you ran under tight timelines. Explain how you balanced statistical rigor with speed, what risks you accepted, what monitoring you implemented to catch negative outcomes early, and what you learned about trade-offs between speed and reliability.
HardTechnical
0 practiced
As a staff-level data scientist, describe how you would influence DoorDash's global data-science roadmap so that team efforts align with company strategy. Address how you would set cross-team priorities, define governance and best practices, measure progress and success, and quantify the impact of cross-team initiatives.
EasyBehavioral
0 practiced
Tell me in two minutes why you want to join DoorDash as a data scientist. Mention which parts of DoorDash's mission or product excite you, and explain how your background and prior work uniquely prepare you to contribute. Be specific about one product area (for example: last-mile logistics, marketplace pricing, or personalization) and describe one measurable impact you would aim to achieve in your first six months.
MediumBehavioral
0 practiced
Tell me about a time when you used data to influence product strategy or a major business decision. Use the STAR format and emphasize: how you framed the problem, which metrics you selected as success criteria, what analyses or models you built, how you presented results to stakeholders, and what the eventual outcome was. Prefer an example relevant to marketplaces, logistics, or personalization if possible.
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