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Design Experiments to Evaluate Courier Initiatives Effectively

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's proficiency in experimental design, causal inference, A/B testing, metric selection, statistical power analysis, and marketplace analytics for two-sided delivery platforms.

  • hard
  • DoorDash
  • Analytics & Experimentation
  • Data Scientist

Design Experiments to Evaluate Courier Initiatives Effectively

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

##### Scenario The team must decide on three courier-related initiatives: ( 1) launch a Top Dasher program that routes higher-quality orders to top performers, ( 2) add extra pay incentives to raise Dasher engagement, and ( 3) switch Dasher compensation from per-order to per-time. ##### Question How would you design an experiment to evaluate the Top Dasher program and determine whether it should be launched? Which metrics and success criteria would you track? 2) How would you precisely define Dasher response rate, what levers could improve it, and how would you structure and analyze an A/B test for the extra pay incentive program? 3) Compare paying Dashers by time versus by completed orders: outline the pros and cons of each model and propose an empirical test to decide which is preferable. ##### Hints Discuss metric selection, experiment design, statistical power, potential biases, and business trade-offs.

Quick Answer: This question evaluates a candidate's proficiency in experimental design, causal inference, A/B testing, metric selection, statistical power analysis, and marketplace analytics for two-sided delivery platforms.

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DoorDash logo
DoorDash
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
85
0

Scenario

You operate a two‑sided delivery marketplace with independent couriers (Dashers). The team must decide on three courier-related initiatives:

  1. Launch a Top Dasher program that routes higher‑quality orders (e.g., shorter distance, higher pay reliability) to top performers.
  2. Add extra pay incentives to raise Dasher engagement.
  3. Switch Dasher compensation from per‑order to per‑time.

Questions

1) Top Dasher program

How would you design an experiment to evaluate the Top Dasher program and determine whether it should be launched? Which metrics and success criteria would you track?

2) Response rate and extra pay incentives

a) How would you precisely define Dasher response rate?

b) What product/ops levers could improve it?

c) How would you structure and analyze an A/B test for the extra pay incentive program?

3) Time pay vs. order pay

Compare paying Dashers by time versus by completed orders: outline the pros and cons of each model and propose an empirical test to decide which is preferable.

Hint: Discuss metric selection, experiment design, statistical power, potential biases (e.g., marketplace spillovers), and business trade‑offs.

Solution

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