Investigate Causes of Increased Driver Wait Time

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 Investigate Causes of Increased Driver Wait Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Investigate Causes of Increased Driver Wait Time

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario DoorDash noticed driver wait time at restaurants spiked last week compared with the previous week. ##### Question Walk me through how you would debug the increase in driver wait time. What metrics, segments, and analyses would you examine to find the root cause? If you confirm restaurant understaffing is responsible, what actions or experiments should DoorDash run to solve the problem? ##### Hints Decompose the end-to-end latency funnel, slice by restaurant, region, time of day, and order mix; compare staffing schedules vs. order volume; propose A/B or operational tests to cut wait.

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 Investigate Causes of Increased Driver Wait Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Investigate Causes of Increased Driver Wait Time

Scenario

DoorDash observed that driver (Dasher) wait time at restaurants spiked last week versus the prior week.

Task

Walk through how you would debug the increase in Dasher wait time. Specify the metrics, segments, and analyses you would use to find the root cause. If you confirm restaurant understaffing is responsible, propose actions or experiments to address the problem.

Notes and expectations

  • Decompose the end-to-end pickup latency funnel and identify which component(s) changed.
  • Slice by restaurant, region, time of day, and order mix; distinguish product vs operational causes.
  • If understaffing is the driver, outline experiments and operational tests to reduce wait time.

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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