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.
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
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)
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?