Prepare for your DoorDash data science interview

Marketplace ML cases and experimentation rounds calibrated to DoorDash’s logistics-first, experiment-driven data culture.

Socratify AI

The Interview

What DoorDash is looking for

ML Case Interview

Marketplace ML Problem

01ML Model Diagnostics & Root Cause

02Feature Engineering for Marketplace

03ETA & Demand Prediction

04Model Monitoring & Drift Detection

Experiment Design Interview

Experimentation & Causal Inference

01A/B Test Design for Two-Sided Markets

02Network Effects & SUTVA

03Metric Selection & Guardrails

04Long-Run vs Short-Run Effects

Behavioral Interview

01Speed & Urgency

02Data-Informed Decision Making

03Cross-Functional Influence

04Customer Obsession

Two-sided marketplace ML cases//Real-time ETA and logistics modeling//Network-effects experiment design

Practice Library

DoorDash exercises

Ready to ace your DoorDash data science interview?

Prepare for your DoorDash data science interview

Marketplace ML cases and experimentation rounds calibrated to DoorDash’s logistics-first, experiment-driven data culture.

Socratify AI

The Interview

What DoorDash is looking for

ML Case Interview

Marketplace ML Problem

01ML Model Diagnostics & Root Cause

02Feature Engineering for Marketplace

03ETA & Demand Prediction

04Model Monitoring & Drift Detection

Experiment Design Interview

Experimentation & Causal Inference

01A/B Test Design for Two-Sided Markets

02Network Effects & SUTVA

03Metric Selection & Guardrails

04Long-Run vs Short-Run Effects

Behavioral Interview

01Speed & Urgency

02Data-Informed Decision Making

03Cross-Functional Influence

04Customer Obsession

Two-sided marketplace ML cases//Real-time ETA and logistics modeling//Network-effects experiment design

How It Works

Master DoorDash Data Science Interviews

Marketplace ML cases and experiment design rounds, scored on DoorDash’s evaluation dimensions. · Click to expand

1Your Data Science Goals

ML Diagnostics

Practitioner

Experiment Design

Practitioner

2Data Science Case Prep

DoorDash EDT accuracy dropped 18% in a new market. Diagnose the ML model and data...

CASE INTERVIEWHard

DoorDash

3Frameworks

Framework

Is model quality degrading (model drift)?

Is input feature distribution shifting?

User session length distribution shifted: median 4.2 min → 1.9 min after app redesign

Training data covers pre-redesign sessions only (data cutoff: 8 months ago)

Are offline metrics (NDCG, MAP) correlated with CTR drop?

NDCG@10 declined from 0.74 to 0.61 in shadow evaluation against live traffic

A/B hold-out shows 2019-era collaborative filter outperforming current model by 14%

Is the feature pipeline producing stale or incorrect signals?

Are real-time behavioral features stale?

User affinity scores update every 6h but browse events lag 4.8h on avg (SLA: 1h)

23% of requests served with affinity scores >12h old during peak load

Is null/missing feature rate rising?

Null rate for "recent-purchase" feature: 3% → 19% after payment-service schema change

Feature monitoring alerts: 0 triggered (alerting threshold set to 25%)

Is serving infrastructure introducing latency or fallback behavior?

Are p99 latency spikes triggering fallback to popularity-based ranking?

Model inference p99: 180ms → 420ms after canary deploy of v3.1 (SLA: 200ms)

38% of requests falling back to non-personalized popularity baseline during peak hours

4Feedback

ML Diagnostics

Distinctive

Statistical Reasoning

Strong

Experiment Design

Meeting Bar

Analytical Communication

Strong

Repeat

Practice Library

DoorDash exercises

DoorDash’s estimated delivery time (EDT) accuracy has dropped 18% in a newly launched...

CASE INTERVIEWHard

Marketplace / Logistics · Model DiagnosticsDoorDash

Design an experiment to test whether a new dasher incentive reduces average delivery...

LEARNHard

Marketplace / Logistics · Experiment DesignDoorDash

Build a feature set for a real-time restaurant recommendation model. Consider...

LEARNMedium

Marketplace / Logistics · Feature EngineeringDoorDash

Tell me about a time you identified a data quality issue that was silently degrading...

BEHAVIORALMedium

Behavioral · Data Quality & OwnershipDoorDash

Describe a situation where you had to make a model deployment decision with incomplete...

BEHAVIORALMedium

Behavioral · Speed & JudgmentDoorDash