Investigate Causes of Cold Food Deliveries and Solutions

Q: Investigate Causes of Cold Food Deliveries and Solutions

This question evaluates a data scientist's competency in diagnostic analytics, causal inference, experiment design, metric definition, funnel analysis, and sample size estimation.

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Question

Diagnosing and Mitigating Cold Food Deliveries

Context

Customers report that delivered food often arrives cold. As the data scientist on the delivery quality (Dasher) team, you must diagnose root causes and design an experiment to test a mitigation (e.g., insulated bags, optimized routing).

Tasks

Investigate root causes of cold deliveries using a delivery funnel (prep → pickup → travel → drop-off).
Define the core metrics and specify which data to pull.
Design an experiment to test a mitigation (e.g., insulated bags, routing/dispatch changes). Include:
- Hypothesis
- Treatment and control
- Unit of randomization and targeting
- Success metrics (primary/secondary/guardrails)
- Runtime and sample size calculation

Hints

Use funnel analysis: prep time, pickup wait, travel time.
Define a quantitative temperature proxy.
Consider a staged A/B test across zones.
Monitor delivery time, reorder rate, and complaint rate.

Investigate Causes of Cold Food Deliveries and Solutions

Overview

Diagnosing and Mitigating Cold Food Deliveries

Context

Tasks

Hints

Solution

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