Instacart·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026Remote

Summary

Interviewed for a DS role at Instacart and got a pretty deep marketplace operations case about same-day delivery being disabled in Miami. One question, but it had three parts and honestly felt like a full onsite loop compressed into 45 minutes.

Questions Asked (1)

Instacart's same-day delivery was disabled on 2 of the last 3 Sunday afternoons in Miami due to insufficient shopper supply. Define the key supply-demand balance metrics you'd track, identify which single leading metric you'd use to trigger disablement and why, build out a root-cause tree with the exact data you'd pull to separate supply drops from demand spikes, and then propose three interventions with full experiment designs including randomization unit, guardrails, and success metrics.

Product Analytics & MetricsRoot Cause AnalysisA/B Testing & Experimentation

Author's notes

This one was brutal in scope.

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AI HintsAI Generated

Suggested Approach

Structure your answer around a clear framework: first define the key supply-demand balance metrics, then select a single leading indicator for disablement, build a root-cause tree with specific data pulls, and finally propose three interventions with rigorous experiment designs. Emphasize the importance of leading metrics and the need to separate supply from demand to avoid misdiagnosis. Be specific about metrics, data sources, and experiment parameters.

Pro tip: When choosing the leading metric for disablement, consider not just the current balance but also the rate of change and forecasted shortfall; a metric like 'projected unfulfilled orders in next hour' can trigger proactive disablement before customer experience degrades.

1. Define Key Supply-Demand Balance Metrics

Identify metrics that capture both supply (shopper availability, active shoppers, fulfillment capacity) and demand (order volume, order rate, backlog). Include balance metrics like fill rate, unfulfilled order rate, and shopper utilization.

2. Select Leading Metric for Disablement Trigger

Choose a single leading metric that predicts imminent failure, such as 'projected unfulfilled orders in the next hour' or 'shopper utilization exceeding threshold with rising backlog'. Explain why it's leading and actionable.

3. Build Root-Cause Tree with Data Pulls

Construct a tree separating supply drops (e.g., shopper cancellations, low login rates) from demand spikes (e.g., promotions, weather events). Specify exact data to pull: historical logs, real-time dashboards, external factors.

4. Propose Three Interventions with Experiment Designs

For each intervention, outline the hypothesis, randomization unit (e.g., user, region, time), guardrail metrics (e.g., customer satisfaction, shopper earnings), and success metrics (e.g., reduction in disablement, fill rate).

Key Points to Mention

Leading vs. lagging metrics: emphasize using leading indicators like projected shortfall to trigger disablement proactively.
Data sources: specify pulling from order management system, shopper app logs, demand forecasting models, and external data (weather, events).
Root-cause separation: use time-series decomposition or causal inference to distinguish supply drops from demand spikes.
Experiment randomization: choose appropriate unit (e.g., geographic region for supply interventions, user-level for demand interventions) to avoid contamination.
Guardrail metrics: include customer wait time, order accuracy, shopper satisfaction, and long-term retention to ensure interventions don't harm other aspects.
Success metrics: define clear primary metrics like reduction in disablement frequency, increase in fill rate, and improvement in shopper utilization.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.