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Diagnose drop in shopper accepted orders

Last updated: Mar 29, 2026

Quick Overview

Evaluates diagnostic root-cause analysis, precise metric definition and instrumentation, observational analytics, and experiment design; Category/domain: Analytics & Experimentation; position type: Data Scientist.

  • medium
  • PayPal
  • Analytics & Experimentation
  • Data Scientist

Diagnose drop in shopper accepted orders

Company: PayPal

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

Instacart notices a sudden issue: on Sunday afternoon, the number of orders **accepted by shoppers** drops by about **2/3** compared to the usual baseline. As the on-call / bar-raiser-style case interviewer, you need to diagnose the problem. 1) Clarify the metric: what exactly does “orders accepted” mean, and what denominator(s) matter? 2) Propose a structured root-cause tree from three perspectives: (a) shopper supply, (b) customer demand, (c) merchant/store operations or platform systems. 3) List the key metrics and slices you would check first (at least 10), and what patterns would support/refute each hypothesis. 4) Describe how you’d determine whether this is a real behavioral change vs a logging/measurement issue. 5) Propose immediate mitigations and longer-term fixes, including at least one experiment or rollout plan to prevent recurrence.

Quick Answer: Evaluates diagnostic root-cause analysis, precise metric definition and instrumentation, observational analytics, and experiment design; Category/domain: Analytics & Experimentation; position type: Data Scientist.

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PayPal
Dec 10, 2025, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
2
0

Instacart notices a sudden issue: on Sunday afternoon, the number of orders accepted by shoppers drops by about 2/3 compared to the usual baseline.

As the on-call / bar-raiser-style case interviewer, you need to diagnose the problem.

  1. Clarify the metric: what exactly does “orders accepted” mean, and what denominator(s) matter?
  2. Propose a structured root-cause tree from three perspectives: (a) shopper supply, (b) customer demand, (c) merchant/store operations or platform systems.
  3. List the key metrics and slices you would check first (at least 10), and what patterns would support/refute each hypothesis.
  4. Describe how you’d determine whether this is a real behavioral change vs a logging/measurement issue.
  5. Propose immediate mitigations and longer-term fixes, including at least one experiment or rollout plan to prevent recurrence.

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