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Instacart·Data Scientist·Technical Phone Screen·Senior

Senior
May 2026

Summary

Instacart DS interview with a meaty experiment design question about launching a new pricing model in a two-sided marketplace. The whole thing was basically one long case and they really wanted to see if you understood why a standard A/B test breaks down here.

Questions Asked (1)

Q1

You're launching a new pricing model to shift shopper behavior toward rush hours in a two-sided marketplace. How would you design an experiment to evaluate it, given that changing prices for some users could affect availability and ETAs for others?

A/B Testing & ExperimentationPricing & MonetizationProduct Analytics & Metrics
Author's notes

This is the kind of question where saying 'randomize users to treatment and control' is basically the wrong answer and they're waiting for you to realize that.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the two-sided nature of the marketplace and the potential for interference between treatment and control groups. Propose a cluster-based randomization (e.g., by geography or time) to minimize spillover, and define clear metrics for both sides (e.g., shopper participation, ETA, order volume). Outline a phased approach: pilot, measure, iterate, and scale if successful.

Pro tip: Emphasize the importance of monitoring guardrail metrics (e.g., overall marketplace health, customer satisfaction) to detect unintended consequences early. Consider using switchback or time-based randomization to account for temporal dynamics in supply and demand.

1. Define Objectives and Hypotheses

Clearly state the goal: shift shopper behavior to rush hours. Formulate hypotheses about how pricing changes will affect both shoppers and customers, including potential trade-offs.

2. Choose Randomization Unit

Select a randomization unit that minimizes interference, such as geographic clusters, time slots (switchback), or shopper cohorts. Avoid individual-level randomization if spillover is likely.

3. Select Metrics and Guardrails

Define primary metrics (e.g., % of orders in rush hours, shopper earnings) and guardrail metrics (e.g., overall delivery time, customer satisfaction, cancellation rates) to monitor marketplace health.

4. Design and Run Experiment

Implement the experiment with appropriate sample size and duration. Consider a phased rollout: pilot in a small region, then expand if results are promising and guardrails are not violated.

5. Analyze and Iterate

Analyze results using appropriate statistical methods (e.g., difference-in-differences, CUPED) to account for interference. If successful, iterate on pricing parameters; if not, diagnose and refine.

Key Points to Mention

  • Interference/spillover effects in two-sided marketplaces
  • Cluster randomization or switchback design
  • Primary and guardrail metrics (e.g., ETA, shopper participation, customer satisfaction)
  • Sample size and power calculations considering cluster randomization
  • Phased rollout and iterative testing
  • Statistical methods to handle interference (e.g., difference-in-differences, CUPED)

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