##### Scenario DoorDash, a food-delivery marketplace, is seeing growing customer complaints about orders arriving late. You are the data scientist asked to diagnose the problem and recommend a fix. ##### Question Diagnose the root causes of delivery delays and design a validation experiment. 1. **Define the problem.** What does "late" mean here, and what is your primary KPI (e.g., on-time delivery rate) versus your secondary / guardrail metrics? Why? 2. **Metrics to examine first.** Which delivery-performance metrics would you look at first, and how do you instrument the order lifecycle so you can localize where the delay happens? 3. **Identify root causes.** How would you attribute excess delay to specific stages (assignment, courier travel, restaurant prep, pickup dwell, drop-off travel) and form hypotheses (courier supply vs. demand, prep-time underestimation, dispatch/batching logic, geography, time-of-day)? 4. **Segment the problem.** Which segments (region/zone, restaurant cohort, courier supply, order attributes, time/weather) would you cut by to localize the issue and prioritize? 5. **Design an experiment / product change.** Propose one solution and design an A/B or geo-holdout test to validate it: unit of randomization, primary outcome, guardrails, duration, power/sample size, and analysis plan. Address interference between nearby zones. ##### Hints Clarify the lateness definition (initial promise vs. latest ETA) to avoid ETA-padding gaming. Decompose end-to-end time into stages and compare each stage to an expected baseline. Segment by region/restaurant/courier/time. Because courier supply is shared across nearby areas, prefer cluster (geo-zone) randomization over per-order randomization, and design for spillovers.

A DoorDash data scientist analytics-and-experimentation screen on diagnosing delivery delays. It asks you to define on-time delivery and the right primary and guardrail metrics, decompose the order lifecycle into stages to localize root causes, segment by region/restaurant/courier/time, and design a cluster-randomized geo experiment to validate a fix.

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This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at DoorDash.

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This question is commonly asked for Data Scientist candidates at DoorDash during technical interviews.

Identify Key Metrics to Address Delivery Delays

Scenario

DoorDash, a food-delivery marketplace, is seeing growing customer complaints about orders arriving late. You are the data scientist asked to diagnose the problem and recommend a fix.

Question

Diagnose the root causes of delivery delays and design a validation experiment.

Define the problem. What does "late" mean here, and what is your primary KPI (e.g., on-time delivery rate) versus your secondary / guardrail metrics? Why?
Metrics to examine first. Which delivery-performance metrics would you look at first, and how do you instrument the order lifecycle so you can localize where the delay happens?
Identify root causes. How would you attribute excess delay to specific stages (assignment, courier travel, restaurant prep, pickup dwell, drop-off travel) and form hypotheses (courier supply vs. demand, prep-time underestimation, dispatch/batching logic, geography, time-of-day)?
Segment the problem. Which segments (region/zone, restaurant cohort, courier supply, order attributes, time/weather) would you cut by to localize the issue and prioritize?
Design an experiment / product change. Propose one solution and design an A/B or geo-holdout test to validate it: unit of randomization, primary outcome, guardrails, duration, power/sample size, and analysis plan. Address interference between nearby zones.

Hints

Clarify the lateness definition (initial promise vs. latest ETA) to avoid ETA-padding gaming. Decompose end-to-end time into stages and compare each stage to an expected baseline. Segment by region/restaurant/courier/time. Because courier supply is shared across nearby areas, prefer cluster (geo-zone) randomization over per-order randomization, and design for spillovers.

Scenario

DoorDash, a food-delivery marketplace, is seeing growing customer complaints about orders arriving late. You are the data scientist asked to diagnose the problem and recommend a fix.

Question

Diagnose the root causes of delivery delays and design a validation experiment.

Define the problem. What does "late" mean here, and what is your primary KPI (e.g., on-time delivery rate) versus your secondary / guardrail metrics? Why?
Metrics to examine first. Which delivery-performance metrics would you look at first, and how do you instrument the order lifecycle so you can localize where the delay happens?
Identify root causes. How would you attribute excess delay to specific stages (assignment, courier travel, restaurant prep, pickup dwell, drop-off travel) and form hypotheses (courier supply vs. demand, prep-time underestimation, dispatch/batching logic, geography, time-of-day)?
Segment the problem. Which segments (region/zone, restaurant cohort, courier supply, order attributes, time/weather) would you cut by to localize the issue and prioritize?
Design an experiment / product change. Propose one solution and design an A/B or geo-holdout test to validate it: unit of randomization, primary outcome, guardrails, duration, power/sample size, and analysis plan. Address interference between nearby zones.

Identify Key Metrics to Address Delivery Delays

Quick Overview

Scenario

Question

Hints

Solution

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Identify Key Metrics to Address Delivery Delays

Quick Overview

Scenario

Question

Hints

Solution

Submit Your Answer to Earn 20XP