Assess Success Criteria for Bike-Courier Delivery Launch
Overview
This question evaluates a data scientist's competency in product analytics, experimentation, marketplace economics, causal inference, metrics definition, and unit-economics assessment for a new delivery feature.
DoorDash
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
60
0
Scenario
DoorDash plans to launch a bike-courier delivery option and wants to assess whether, where, and how to roll it out successfully.
Task
As the data scientist leading this, address the following:
- Why launch a bike delivery option from DoorDash’s perspective? Who benefits and how?
- What criteria would define success for the bike feature (customers, dashers, merchants, platform economics)?
- How would you decide between launching in all markets versus focusing on urban/downtown areas?
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Which metrics would you track to judge success?
- Short-term (immediate operational/experience effects)
- Long-term (retention, network growth, unit economics)
- Guardrails (safety, quality, marketplace balance)
- How would you evaluate the feature’s impact while accounting for marketplace/network effects (multi-sided platform dynamics)?
- How would you design and run an A/B test (or alternative experiment) to measure effectiveness, including unit of randomization, spillover controls, power, and analysis plan?
- What data would you gather to assess downtown market suitability for bikes?
Hint: Consider marketplace balance for consumers, dashers, and restaurants; efficiency, cost, delivery time, pilot-market choice, and robust experiment design.
Solution
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