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How to decide if users need a new feature

Last updated: Mar 29, 2026

Quick Overview

This prompt evaluates experimentation design, causal inference, product analytics, and instrumentation skills; it falls under Analytics & Experimentation and targets applied product-level data science at a strategic/analytical abstraction level, commonly asked to assess judgment on metric choice, segmentation, and trade-off reasoning.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

How to decide if users need a new feature

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

You are a Data Scientist at a social app. The product team proposes a new in-app feature (e.g., a new sharing surface). You have event-level data and can run experiments. Answer the following: 1) **Using the data you already have (observational / pre-launch data), how would you identify whether users need this feature?** - Be explicit about what “need” means and what signals would support it. - Include how you would segment users and avoid misleading conclusions. 2) **Assume you can obtain any data you want. What additional data would you collect to answer (1) better?** - Include instrumentation you would add and any qualitative inputs you’d combine with quantitative data. 3) **How would you measure whether the feature is successful after launch?** - Define a North Star metric and 2–4 supporting/guardrail metrics. - Describe an A/B test design (randomization unit, duration, power/MDE considerations). 4) **How would you clarify the feature goal and trade-offs with stakeholders?** - Give examples of common trade-offs (e.g., engagement vs. quality, retention vs. spam/abuse, creator vs. consumer outcomes). 5) **How would you detect and handle novelty effects?** - Describe what you would look for in time series and what analysis or experimental design changes you would use to ensure the result is durable. Assumptions: users can generate multiple events per day; you can track impressions, clicks, downstream actions, and retention. State any additional assumptions you need.

Quick Answer: This prompt evaluates experimentation design, causal inference, product analytics, and instrumentation skills; it falls under Analytics & Experimentation and targets applied product-level data science at a strategic/analytical abstraction level, commonly asked to assess judgment on metric choice, segmentation, and trade-off reasoning.

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Meta
Nov 2, 2025, 12:00 AM
Data Scientist
Technical Screen
Analytics & Experimentation
2
0

You are a Data Scientist at a social app. The product team proposes a new in-app feature (e.g., a new sharing surface). You have event-level data and can run experiments.

Answer the following:

  1. Using the data you already have (observational / pre-launch data), how would you identify whether users need this feature?
    • Be explicit about what “need” means and what signals would support it.
    • Include how you would segment users and avoid misleading conclusions.
  2. Assume you can obtain any data you want. What additional data would you collect to answer (1) better?
    • Include instrumentation you would add and any qualitative inputs you’d combine with quantitative data.
  3. How would you measure whether the feature is successful after launch?
    • Define a North Star metric and 2–4 supporting/guardrail metrics.
    • Describe an A/B test design (randomization unit, duration, power/MDE considerations).
  4. How would you clarify the feature goal and trade-offs with stakeholders?
    • Give examples of common trade-offs (e.g., engagement vs. quality, retention vs. spam/abuse, creator vs. consumer outcomes).
  5. How would you detect and handle novelty effects?
    • Describe what you would look for in time series and what analysis or experimental design changes you would use to ensure the result is durable.

Assumptions: users can generate multiple events per day; you can track impressions, clicks, downstream actions, and retention. State any additional assumptions you need.

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