Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta DS analytics round, one big open-ended question about comment engagement. The kind of question that sounds manageable until you realize how many directions it can go.

Questions Asked (1)

Given post, comment, and user tables, how would you analyze how comments are distributed across users to measure engagement? What metrics would you define, and how would you evaluate the impact of a new comment feature?

Product Analytics & MetricsA/B Testing & Experimentation

Author's notes

I started with comments per DAU which felt right, but the follow-up pushed me toward distribution shape, not just averages.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the tables and defining engagement metrics that capture both volume and distribution of comments across users. Then propose an analysis plan to measure the current distribution and design an A/B test to evaluate the new feature's impact on those metrics.

Pro tip: Emphasize the importance of segmenting users by activity level (e.g., power users vs. casual) to avoid averaging away heterogeneous effects, and consider network effects since comments are social.

1. Clarify data and define engagement

Understand the schema: post, comment, user tables. Define engagement as a combination of comment volume, user participation breadth, and interaction depth.

2. Analyze current distribution

Compute metrics like comments per user, Gini coefficient or Lorenz curve to measure inequality, and identify power users vs. casual commenters.

3. Define success metrics for new feature

Select primary metrics (e.g., average comments per user, percentage of users commenting) and guardrail metrics (e.g., spam rate, user retention).

4. Design A/B test

Randomize users into control and treatment groups, ensure sufficient power, and run the test for a suitable duration to capture novelty effects.

5. Evaluate impact and iterate

Analyze results with statistical tests, segment by user activity, and consider long-term effects. Recommend next steps based on findings.

Key Points to Mention

Gini coefficient or Lorenz curve to measure comment distribution inequality
Segmentation by user activity level (e.g., power users, casual users, lurkers)
A/B testing best practices: randomization, sample size, novelty effects
Guardrail metrics to ensure feature doesn't harm user experience (e.g., spam, negativity)
Network effects and social dynamics that may influence comment behavior
Long-term holdout or cohort analysis to measure sustained impact

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