Diagnose Decline in Delivery Success: Data, Hypotheses, Tests
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 Decline in Delivery Success: Data, Hypotheses, Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Diagnose Decline in Delivery Success: Data, Hypotheses, Tests
Company: DoorDash
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
As a regional manager you observe that the number of successful deliveries in one territory has declined by 10%.
##### Question
How would you diagnose the 10% drop in successful deliveries? Outline the data cuts, hypotheses, and statistical tests you would use.
##### Hints
Talk through segmentation, time-series vs cross-section checks, customer/merchant/dasher metrics, and significance thresholds.
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 Decline in Delivery Success: Data, Hypotheses, Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Diagnose Decline in Delivery Success: Data, Hypotheses, Tests
Diagnose a 10% Drop in Successful Deliveries
Scenario
You manage a territory in a food-delivery marketplace and observe that the number of successful deliveries has dropped by 10% in that territory compared to the recent baseline.
Assume “success rate” = successful deliveries / attempted deliveries. Clarify whether the 10% drop is relative (e.g., 90% → 81%) or absolute (e.g., 90% → 80%). If not specified, state how you would handle this ambiguity.
Question
How would you diagnose the 10% drop in successful deliveries? Outline:
The data cuts/segmentations you would run.
Your key hypotheses for root causes.
The statistical tests (and thresholds) you would apply, including time-series vs. cross-sectional checks.
Hints
Discuss segmentation, time-series vs. cross-section checks, and which customer/merchant/dasher metrics you’d examine, along with significance thresholds.
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?