Diagnose Causes of High Out-of-Stock Rate in Groceries
Quick Overview
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Diagnose Causes of High Out-of-Stock Rate in Groceries states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Diagnose Causes of High Out-of-Stock Rate in Groceries
Company: DoorDash
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
##### Scenario
DoorDash onsite case interview assessing product and operational metrics decision-making.
##### Question
Our new grocery vertical shows a high out-of-stock rate; diagnose causes and propose solutions. DoorDash currently limits deliveries to 3 miles. Should we extend the radius? Describe analysis and metrics used. Should we offer free delivery for selected restaurants to non-members? How would you evaluate this?
##### Hints
Frame hypotheses, identify key metrics, design experiments, model operational cost vs. revenue impact.
Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Diagnose Causes of High Out-of-Stock Rate in Groceries states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
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
Diagnose the causes of high OOS in the grocery vertical and propose solutions. Specify hypotheses, key metrics, and analyses you would run.
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.
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
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?