Investigate Causes of Cold Meal Deliveries

Quick Overview

DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.

Investigate Causes of Cold Meal Deliveries

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A delivery service is receiving customer complaints that meals arrive cold. ##### Question Customers complain their food is cold on delivery. How would you investigate and solve this? Detail data needed, metrics, analyses/experiments, and operational or product changes you’d recommend. ##### Hints Think root-cause analysis, A/B testing, delivery logistics, packaging, driver routing.

Overview: DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.

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DoorDash
Jul 12, 2025
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Investigate and Reduce Cold Food Deliveries

A delivery service is receiving customer complaints that meals arrive cold. You need to investigate the root causes and recommend data-driven product or operations changes.

Constraints & Assumptions

  • Complaints are noisy and may reflect reporting bias; include objective proxies where possible.
  • Consider the full delivery lifecycle from food ready time to customer handoff.
  • Diagnose before prescribing a solution.
  • Preserve marketplace health while reducing cold-food experiences.

Clarifying Questions to Ask Guidance

  • Did the complaint count increase, the complaint rate increase, or both?
  • Is the issue concentrated by merchant, cuisine, distance, courier mode, city, daypart, or weather?
  • Do we have timestamps for food ready, dasher arrival, pickup, dropoff, and handoff?
  • Were there recent changes in batching, routing, packaging, merchant operations, or support reason codes?

What a Strong Answer Covers Guidance

  • Data needed from orders, merchants, couriers, customers, platform dispatch, weather, GPS, packaging, support, refunds, and ratings.
  • Metrics: cold complaint rate, refund rate, CSAT, cook-to-pickup time, pickup-to-dropoff time, total heat-risk time, batch size, stack position, detours, distance, packaging compliance, and courier bag usage.
  • Root-cause analyses by segment, time-series change point, funnel decomposition, and contribution analysis.
  • Data-quality checks for taxonomy changes, missing timestamps, denominator shifts, support-policy changes, and underreporting.
  • Candidate fixes such as dispatch timing, batching limits, insulated bags, packaging improvements, merchant throttling, route optimization, and prep-time prediction.
  • Experiment design with treatment/control, unit of randomization, primary metric, guardrails, duration, sample size, and rollout plan.
  • Guardrails for delivery time, courier earnings, cost, merchant burden, cancellation, food quality, and customer retention.

Follow-up Questions Guidance

  • How would you distinguish restaurant-side cooling from transit-side cooling?
  • Which segment should receive the first intervention?
  • How would you validate a predicted temperature-risk score?
  • What if the fix reduces cold complaints but increases delivery time?
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