You are interviewing for a product data role at DoorDash. Consider the following marketplace scenarios.
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Top Dasher program
DoorDash runs a status program for high-performing Dashers (for example, better access to scheduling or order opportunities).
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What are the main product and marketplace pros and cons of such a program?
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How would you define success metrics? Include a primary metric, supporting metrics, and guardrails across the consumer, dasher, and merchant sides of the marketplace.
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What should the randomization unit be for an experiment: dasher, order, market, or time-based switchback? Explain the trade-offs and any interference concerns.
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Suppose the treatment group's key metric is lower than the control group's. How would you investigate whether this is a true negative impact versus a data quality issue, power issue, imbalance, novelty effect, or spillover?
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Order cancellation rate has increased
DoorDash observes that overall order cancellation rate is materially higher than usual.
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Which teams or parts of the organization are affected?
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How would you diagnose the increase? Build a structured analysis plan to identify root causes.
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What hypotheses would you test, and how would you validate them?
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In your answer, consider funnel stage, who initiated the cancellation, market and time segmentation, merchant quality, inventory availability, delivery ETA, dasher supply, weather, app performance, and recent policy or product changes.
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Merchant self-serve promotions vs. automatic promotions
DoorDash is deciding between two systems: merchants configure promotions manually, or DoorDash automatically recommends or sets up promotions for them.
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What are the pros and cons of each approach?
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How would you design an experiment to compare them?
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What randomization unit would you choose, and why?
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What trade-offs would you watch for, such as adoption, merchant trust, cannibalization, incremental GMV, contribution margin, customer discount dependency, and heterogeneous effects by merchant type or market?