##### Scenario As a product/analytics lead at a food-delivery marketplace you must evaluate several Dasher-facing initiatives (Top-Dasher prioritization, Extra-Pay incentives, and switching pay model from per-order to per-time) before deciding whether to launch them. ##### Question How would you assess whether the Top-Dasher program should be launched? 2) For an "extra pay" incentive aimed at improving Dasher engagement: a) what primary success metric(s) would you track? b) design an A/B test (including treatment, control, experiment length, sample-size, and guardrail metrics). 3) The company is considering switching Dasher compensation from per-order to per-time. What are the key pros & cons of each model, and how would you experimentally validate which model is better for marketplace health? ##### Hints Discuss causal identification, experiment vs. quasi-experiment trade-offs, KPI definition (accept rate, fulfillment time, retention), supply-demand balance, cost impact, and possible negative externalities.

This question evaluates a data scientist's skills in experimentation, causal identification, KPI definition, and measuring multi-sided marketplace impacts such as fulfillment, engagement, retention, and unit economics.

How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

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 Dasher Initiatives with A/B Testing and Metrics

Scenario

You are the product/analytics lead for a food-delivery marketplace. You must evaluate several Dasher-facing initiatives before deciding whether to launch them:

Top-Dasher prioritization: preferentially prioritize “top” couriers in dispatch.
Extra-Pay incentives: targeted pay boosts to increase engagement in specific zones/times.
Switching pay model: from per-order to per-time compensation.

Assumptions (for clarity):

“Top-Dasher” = a courier who meets defined reliability/quality thresholds (e.g., high completion rate, on-time rate, low cancel rate) and would receive higher dispatch priority in treatment areas.
“Marketplace health” blends outcomes across consumer, merchant, and courier experiences alongside unit economics.

Questions

How would you assess whether the Top-Dasher program should be launched?
For an extra-pay incentive aimed at improving Dasher engagement: a) What primary success metric(s) would you track? b) Design an A/B test (treatment/control, randomization unit, experiment length, sample size, and guardrail metrics).
The company is considering switching Dasher compensation from per-order to per-time. What are the key pros and cons of each model, and how would you experimentally validate which model is better for marketplace health?

Hints

Discuss causal identification (experiments vs. quasi-experiments), KPI definition (accept rate, fulfillment time, retention), supply–demand balance, cost impact, and potential negative externalities (gaming, spillovers, fairness).

Scenario

You are the product/analytics lead for a food-delivery marketplace. You must evaluate several Dasher-facing initiatives before deciding whether to launch them:

Top-Dasher prioritization: preferentially prioritize “top” couriers in dispatch.

Extra-Pay incentives: targeted pay boosts to increase engagement in specific zones/times.

Switching pay model: from per-order to per-time compensation.

Assumptions (for clarity):

“Top-Dasher” = a courier who meets defined reliability/quality thresholds (e.g., high completion rate, on-time rate, low cancel rate) and would receive higher dispatch priority in treatment areas.

“Marketplace health” blends outcomes across consumer, merchant, and courier experiences alongside unit economics.

Questions

How would you assess whether the Top-Dasher program should be launched?

For an extra-pay incentive aimed at improving Dasher engagement: a) What primary success metric(s) would you track? b) Design an A/B test (treatment/control, randomization unit, experiment length, sample size, and guardrail metrics).

The company is considering switching Dasher compensation from per-order to per-time. What are the key pros and cons of each model, and how would you experimentally validate which model is better for marketplace health?

Evaluate Dasher Initiatives with A/B Testing and Metrics

Quick Overview

Scenario

Questions

Hints

Solution

Submit Your Answer to Earn 20XP

Evaluate Dasher Initiatives with A/B Testing and Metrics

Quick Overview

Scenario

Questions

Hints

Solution

Submit Your Answer to Earn 20XP