Evaluate Biker Feature Success
Company: DoorDash
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
DoorDash is considering launching a Biker Mode feature for Dashers who deliver by bicycle. The feature may help bicycle Dashers identify suitable short-distance orders, receive bike-friendly routing, and get matched to deliveries where bicycles are operationally efficient.
As the data scientist for this launch, answer the following:
1. How would you define success for the Biker Mode feature?
2. What primary metrics, secondary metrics, and guardrail metrics would you track?
3. How would you design an experiment to estimate the causal impact of the feature?
4. What biases, confounders, marketplace interference issues, or edge cases would you watch for?
5. How would you decide whether to launch, iterate, or roll back the feature?
Quick Answer: This question evaluates a data scientist's competency in defining product success criteria, selecting primary/secondary/guardrail metrics, designing causal experiments, and identifying biases, confounders, and marketplace interference for a delivery-focused feature.