Evaluate Top-Dasher Program's Benefits and Challenges
Overview
This question evaluates proficiency in experimental design, causal inference, marketplace economics, metric-driven product analytics, and profitability modeling for a Data Scientist role within Analytics & Experimentation.
DoorDash
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
55
0
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.
Solution
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