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Diagnose Sunday Miami same‑day outages

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in marketplace analytics, causal inference, metric engineering, and experiment design by asking for precise supply–demand balance metrics, a root‑cause analysis, and randomized interventions for repeated same‑day disablements in a regional market.

  • hard
  • Instacart
  • Analytics & Experimentation
  • Data Scientist

Diagnose Sunday Miami same‑day outages

Company: Instacart

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

Instacart offers same‑day or scheduled delivery; same‑day is disabled when shopper supply is insufficient. Data show that on 2 of the last 3 Sunday afternoons in Miami, same‑day was disabled. a) Define precise supply–demand balance metrics: real‑time shopper‑to‑order ratio, fill rate within SLA, queueing delay distribution, shopper acceptance rate, and effective capacity after time‑to‑shop and travel constraints. Which single leading metric would you use to trigger disablement and why? b) Lay out a root‑cause tree with the exact data you’d pull (traffic by minute, batch creation rate, shopper online/active, substitution complexity, store closures, pay rates, weather, events), and how you’d separate demand spikes from supply drops causally (e.g., instrument with exogenous weather, difference‑in‑differences vs other markets). c) Propose 3 interventions (supply incentives, scheduled‑to‑same‑day rebalancing, demand shaping via ETAs/fees) and for each, design an experiment with eligibility, randomization unit, guardrails (cancellations, NPS), and success metrics; quantify expected impact and key risks (cannibalization, fairness, marketplace instability).

Quick Answer: This question evaluates a data scientist's competency in marketplace analytics, causal inference, metric engineering, and experiment design by asking for precise supply–demand balance metrics, a root‑cause analysis, and randomized interventions for repeated same‑day disablements in a regional market.

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Instacart logo
Instacart
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Analytics & Experimentation
4
0

Marketplace stability case: same‑day disablement on Sunday afternoons (Miami)

Context

Instacart offers two fulfillment modes:

  • Same‑day delivery (real‑time dispatch)
  • Scheduled delivery (future time windows)

When shopper supply is insufficient to maintain service levels, same‑day can be temporarily disabled. In Miami, same‑day was disabled on 2 of the last 3 Sunday afternoons.

Tasks

(a) Define precise supply–demand balance metrics: real‑time shopper‑to‑order ratio, fill rate within SLA, queueing delay distribution, shopper acceptance rate, and effective capacity after time‑to‑shop and travel constraints. Choose one leading metric to trigger disablement and justify.

(b) Build a root‑cause tree. Specify the exact data you would pull (e.g., traffic by minute, batch creation rate, shopper online/active counts, substitution complexity, store closures, pay rates, weather, local events). Explain how you would causally separate demand spikes from supply drops (e.g., instrument with exogenous weather, difference‑in‑differences vs. other markets).

(c) Propose three interventions—(1) supply incentives, (2) scheduled‑to‑same‑day rebalancing, (3) demand shaping via ETAs/fees—and for each, design an experiment: eligibility, randomization unit, guardrails (cancellations, NPS), success metrics, expected impact, and key risks (cannibalization, fairness, marketplace instability).

Solution

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