Diagnose Causes of High Out-of-Stock Rate in Groceries
Product and Operations Case: Grocery OOS, Delivery Radius, and Free Delivery
Context
You are a data scientist in an onsite analytics and experimentation interview. The marketplace recently launched a new grocery vertical and is observing elevated out-of-stock (OOS) rates. Current delivery radius is capped at 3 miles. There is also interest in offering free delivery for selected restaurants to non-members.
Assume you have access to order logs, item-level pick events, substitutions, cancellations, refunds, customer support contacts, dasher time-and-distance telemetry, store metadata (inventory feeds, hours), and experiment platforms that support geo and user-level randomization.
Tasks
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Diagnose the causes of high OOS in the grocery vertical and propose solutions. Specify hypotheses, key metrics, and analyses you would run.
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The current delivery radius is limited to 3 miles. Should we extend the radius? Describe the analysis and metrics you would use, including how you would model operational cost versus revenue impact, and how you would test the change.
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Should we offer free delivery for selected restaurants to non-members? How would you evaluate this? Outline experiment design, success metrics, and unit economics.
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
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State assumptions about instrumentation, randomization, sample size, and data quality.
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Separate descriptive analysis from causal claims.
What a Strong Answer Covers
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A metric framework with primary, guardrail, and diagnostic metrics.
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A credible analysis or experiment design with clear assumptions and bias checks.
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SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
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An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions
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What sanity checks would you run before trusting the result?
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How would you handle novelty effects, seasonality, or selection bias?
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What decision would you make if metrics disagree?