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Size opportunity for new product line

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's skills in financial opportunity sizing, sensitivity analysis, and experimental design by requiring quantitative modeling of impacted users, incremental orders, revenue, and gross profit for a proposed product line.

  • Medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Size opportunity for new product line

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: Medium

Interview Round: Onsite

An e-commerce site considers adding a "Home Office" product line. Before any A/B test, size the opportunity and recommend whether to proceed. Assumptions (last quarter): 8M monthly active buyers; 30% browse adjacent categories monthly; their baseline purchase rate is 6%; AOV $60; gross margin 25%; expected attach of 0.5 items/purchase at $20/item; fixed setup cost $2M; variable cost $1 per browsing user reached; expected conversion uplift from better relevance and supply is +15% relative among the 30% segment. Tasks: - Estimate the monthly impacted population, incremental orders, revenue, and gross profit; include attach effect. - Compute break-even months and sensitivity to ±20% errors in AOV and uplift. - Identify the 3 most critical assumptions and how you would validate each with pre-experiment analyses (logs, surveys, cohort analysis) before committing engineering resources. - Propose the initial experiment design (targeting, success and guardrail metrics, exposure ramp) if the pre-test business case clears the hurdle.

Quick Answer: This question evaluates a data scientist's skills in financial opportunity sizing, sensitivity analysis, and experimental design by requiring quantitative modeling of impacted users, incremental orders, revenue, and gross profit for a proposed product line.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Analytics & Experimentation
5
0

An e-commerce site considers adding a "Home Office" product line. Before any A/B test, size the opportunity and recommend whether to proceed. Assumptions (last quarter): 8M monthly active buyers; 30% browse adjacent categories monthly; their baseline purchase rate is 6%; AOV 60;grossmargin2560; gross margin 25%; expected attach of 0.5 items/purchase at 60;grossmargin2520/item; fixed setup cost 2M;variablecost2M; variable cost 2M;variablecost1 per browsing user reached; expected conversion uplift from better relevance and supply is +15% relative among the 30% segment. Tasks:

  • Estimate the monthly impacted population, incremental orders, revenue, and gross profit; include attach effect.
  • Compute break-even months and sensitivity to ±20% errors in AOV and uplift.
  • Identify the 3 most critical assumptions and how you would validate each with pre-experiment analyses (logs, surveys, cohort analysis) before committing engineering resources.
  • Propose the initial experiment design (targeting, success and guardrail metrics, exposure ramp) if the pre-test business case clears the hurdle.

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