Determine Optimal Dasher Compensation Model and Diagnose Metric Drops

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Optimal Dasher Compensation Model and Diagnose Metric Drops states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Determine Optimal Dasher Compensation Model and Diagnose Metric Drops

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

##### Scenario DoorDash operates a three-sided marketplace (consumers, dashers, merchants). Leadership is debating whether to shift dasher compensation from per-order payments to an hourly (time-based) model and also wants a framework for diagnosing sudden drops in key metrics. ##### Question How would you determine whether DoorDash should pilot paying dashers by time instead of by order? Describe the experiment design, success metrics, and how you would control for marketplace effects across consumers, merchants, and dashers. Suppose a critical marketplace metric (e.g., order completion rate) suddenly declines. Walk through a structured process to identify the root cause and quantify its impact. ##### Hints Cover A/B test setup, sampling, guardrail metrics, segment analysis, and hypotheses tree for root-cause analysis; address external factors and data instrumentation issues.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Optimal Dasher Compensation Model and Diagnose Metric Drops states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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DoorDash
Aug 4, 2025, 10:55 AM
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Determine Optimal Dasher Compensation Model and Diagnose Metric Drops

Time-Based Dasher Pay Pilot and Marketplace Root-Cause Analysis

Context

DoorDash is a three-sided marketplace (consumers, dashers, merchants). Leadership is considering shifting dasher compensation from per-order to time-based (hourly) pay. They also want a rigorous framework to diagnose sudden drops in critical marketplace metrics.

Task

  1. Should DoorDash pilot paying dashers by time instead of by order? Propose an experiment design that covers:
  • Treatment definition, randomization unit, sampling/geo selection, spillover control
  • Primary success metrics, cost metrics, and guardrail metrics across consumers, dashers, and merchants
  • Segmentation and heterogeneity analysis
  • Ramp, power/MDE, and risk mitigation
  • How to control for broader marketplace effects (supply-demand equilibrium, pricing/dispatch interactions, seasonality, external shocks)
  1. Suppose a critical marketplace metric (e.g., order completion rate) suddenly declines. Describe a structured, step-by-step process to:
  • Localize and identify root cause(s) across consumers, dashers, merchants, platform/instrumentation, and external factors
  • Quantify business impact and prioritize mitigations

Hints: Include A/B test setup, guardrail metrics, segment analysis, hypotheses tree, and considerations for external factors and data instrumentation.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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