##### Scenario DoorDash is considering a 'Biker Dasher' program to let couriers use bicycles (and e-bikes) for deliveries in dense urban areas. You are the data scientist supporting the launch. ##### Question Work through the end-to-end design, launch, measurement, and post-launch diagnosis of this program. 1. What is the primary business objective of launching the Biker Dasher program? 2. What factors should you evaluate before launching (market/geo fit, supply-demand balance, operational readiness, economics, safety/compliance)? 3. How would you operationalize this program and make it functional day-to-day and at scale (eligibility, onboarding, routing/ETA, dispatch, pricing, merchant ops, support)? 4. What specific data would you collect to analyze parking or curb-space situations for biker dashers? 5. What metrics would you track to measure the program's success? Specify core KPIs, secondary metrics, and guardrails. 6. How would you design the experiment to test the feature, given the two-sided marketplace and network interference? 7. How would you decide the geographic selection and phased rollout strategy? 8. If the metrics do not improve, how would you diagnose the outcome and iterate? ##### Hints Define objectives and pre-launch factors; consider supply, demand, and city constraints; design KPIs with guardrails; use geo/switchback experiments to handle marketplace interference; plan a metric-gated phased rollout; and have a structured root-cause path for a null result.

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 Determine Success Metrics for Biker Dasher Program Launch states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at DoorDash.

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This question is commonly asked for Data Scientist candidates at DoorDash during technical interviews.

Determine Success Metrics for Biker Dasher Program Launch

Scenario

DoorDash is considering a 'Biker Dasher' program to let couriers use bicycles (and e-bikes) for deliveries in dense urban areas. You are the data scientist supporting the launch.

Question

Work through the end-to-end design, launch, measurement, and post-launch diagnosis of this program.

What is the primary business objective of launching the Biker Dasher program?
What factors should you evaluate before launching (market/geo fit, supply-demand balance, operational readiness, economics, safety/compliance)?
How would you operationalize this program and make it functional day-to-day and at scale (eligibility, onboarding, routing/ETA, dispatch, pricing, merchant ops, support)?
What specific data would you collect to analyze parking or curb-space situations for biker dashers?
What metrics would you track to measure the program's success? Specify core KPIs, secondary metrics, and guardrails.
How would you design the experiment to test the feature, given the two-sided marketplace and network interference?
How would you decide the geographic selection and phased rollout strategy?
If the metrics do not improve, how would you diagnose the outcome and iterate?

Hints

Define objectives and pre-launch factors; consider supply, demand, and city constraints; design KPIs with guardrails; use geo/switchback experiments to handle marketplace interference; plan a metric-gated phased rollout; and have a structured root-cause path for a null result.

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

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

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

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?