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Increase posts receiving one comment

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in product analytics and experimentation, specifically metric definition and guardrails, segmentation, hypothesis generation, prioritization frameworks (ICE/RICE), randomized test design, and causal diagnostics for engagement metrics.

  • Medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Increase posts receiving one comment

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: Medium

Interview Round: Technical Screen

Goal: Increase the share of group posts that receive ≥1 comment within 48 hours. Assume today is 2025-09-01. (a) Precisely define the primary metric and 3+ guardrails (e.g., commenter spam rate, creator report rate, time‑to‑first‑comment). (b) Outline the analysis structure: key tables/fields you’d inspect, a user‑journey funnel from post creation → impressions → opens → dwell → comment, and at least 6 meaningful segments (e.g., new vs veteran posters, content type, group size deciles). (c) Brainstorm at least 10 interventions across supply, demand, matching, notifications, incentives, and UX; for each, state the hypothesized mechanism. (d) Prioritize with a scoring framework (ICE/RICE) including explicit assumptions. (e) Pick your top idea and design an A/B test: unit of randomization, sample size/power assumptions, success window, duration, and interference/spillover handling within groups. (f) If impressions increase but comments do not ("only impression no comment"), propose diagnostics, metric decomposition, and a follow‑up test plan that distinguishes intent, friction, and quality issues.

Quick Answer: This question evaluates a data scientist's competency in product analytics and experimentation, specifically metric definition and guardrails, segmentation, hypothesis generation, prioritization frameworks (ICE/RICE), randomized test design, and causal diagnostics for engagement metrics.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
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Goal: Increase the share of group posts that receive ≥1 comment within 48 hours. Assume today is 2025-09-01. (a) Precisely define the primary metric and 3+ guardrails (e.g., commenter spam rate, creator report rate, time‑to‑first‑comment). (b) Outline the analysis structure: key tables/fields you’d inspect, a user‑journey funnel from post creation → impressions → opens → dwell → comment, and at least 6 meaningful segments (e.g., new vs veteran posters, content type, group size deciles). (c) Brainstorm at least 10 interventions across supply, demand, matching, notifications, incentives, and UX; for each, state the hypothesized mechanism. (d) Prioritize with a scoring framework (ICE/RICE) including explicit assumptions. (e) Pick your top idea and design an A/B test: unit of randomization, sample size/power assumptions, success window, duration, and interference/spillover handling within groups. (f) If impressions increase but comments do not ("only impression no comment"), propose diagnostics, metric decomposition, and a follow‑up test plan that distinguishes intent, friction, and quality issues.

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