##### Scenario DoorDash is considering several driver-facing initiatives: a Top-Dasher status, cash incentives, and a tiered rewards program. ##### Question For a potential Top-Dasher program, outline its key advantages and drawbacks and state whether you would launch it. If launched, what experiment or measurement framework would you use to quantify impact and define success? DoorDash wants to pay cash incentives to Dashers. How would you design a test to determine causal lift and decide whether the incentive is profitable? Propose a structure for a broader Dasher rewards program and explain how you would measure its effectiveness and iterate on it. ##### Hints Define treatment and control, choose primary and guardrail metrics (GMV, fulfillment rate, cost per order), compute incremental profit, check sample-size/power, consider driver selection bias, geographic stratification, seasonality, and long-term retention effects.

Evaluate Top-Dasher Program's Benefits and Challenges evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at DoorDash.

What role is this question designed for?

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

Evaluate Top-Dasher Program's Benefits and Challenges

Scenario

DoorDash is considering several driver-facing initiatives: a Top-Dasher status, cash incentives, and a tiered rewards program.

Questions

Top-Dasher program

Outline key advantages and drawbacks of a potential Top-Dasher status.
Make a go/no-go launch recommendation.
If launched, describe the experiment or measurement framework you would use to quantify impact and define success.

Cash incentives

DoorDash wants to pay cash incentives to Dashers. How would you design a test to determine causal lift and decide whether the incentive is profitable?

Broader Dasher rewards program

Propose a structure for a broader tiered rewards program and explain how you would measure its effectiveness and iterate.

Guidance to consider

Clearly define treatment and control; account for marketplace interference (supply-demand interactions).
Choose primary and guardrail metrics (e.g., GMV, fulfillment rate, cost per order, wait times, cancellations, Dasher retention).
Compute incremental profit, not just activity lift.
Check sample size/power; stratify by geography/time; handle seasonality.
Address selection bias, geographic stratification, and long-term retention effects.

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?