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Meta·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Meta DS interview with a product analytics focus. The whole session was basically one meaty scenario about push notifications, which sounds narrow until you realize how many directions it can go.

Questions Asked (2)

Q1

What metrics would you track to evaluate the quality of push notifications in a consumer app, and how would you define thresholds for what counts as 'high quality'?

Product Analytics & MetricsA/B Testing & Experimentation
Author's notes

I went straight to open rate and click-through, which felt obvious the moment I said it out loud.

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AI HintsAI Generated

Suggested Approach

Start by framing push notifications as a product feature with a clear goal: to drive user engagement and retention without causing annoyance. Then, propose a balanced set of metrics across engagement, retention, and user experience, and explain how to set thresholds using baselines, business goals, and experimentation.

Pro tip: Emphasize that thresholds should be dynamic and context-dependent, and that you would use A/B tests to validate them. Also, mention the importance of monitoring unsubscribe rates and negative feedback as guardrail metrics.

1. Define the goal of push notifications

Clarify that push notifications aim to re-engage users, drive specific actions, and improve retention, while avoiding annoyance.

2. Identify key metrics across dimensions

List metrics for engagement (CTR, conversion rate), retention (DAU/MAU lift, churn reduction), and user experience (opt-out rate, negative feedback).

3. Establish thresholds using baselines and goals

Set thresholds by analyzing historical performance, industry benchmarks, and business objectives, ensuring they are realistic and actionable.

4. Validate and iterate with experiments

Use A/B tests to measure the impact of notifications on key metrics and refine thresholds based on results.

5. Monitor and adjust over time

Continuously track metrics and thresholds, adapting to changes in user behavior and business priorities.

Key Points to Mention

  • Click-through rate (CTR) and conversion rate as primary engagement metrics
  • Retention metrics such as DAU/MAU and churn rate
  • User experience metrics like opt-out rate and negative feedback (e.g., app uninstalls)
  • Thresholds should be based on baselines, business goals, and A/B test results
  • Guardrail metrics to ensure notifications don't harm user experience
  • Segmentation by user demographics or behavior to tailor thresholds

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

Q2

Design an experiment to evaluate a new push notification algorithm at the time of its launch.

A/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

This part I felt better about.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of the new push notification algorithm (e.g., increase click-through rate or user engagement) and define a clear hypothesis. Then outline a randomized controlled experiment (A/B test) with proper randomization, control, and success metrics, considering guardrail metrics and potential network effects. Finally, discuss analysis plan including power analysis, duration, and how to handle novelty effects at launch.

Pro tip: At launch, novelty effects and seasonality can confound results, so plan for a longer test or use a holdback group to measure long-term impact. Also, consider using a switchback or cluster randomization if interference is a concern.

1. Define Objective and Hypothesis

Clarify the primary goal (e.g., increase CTR) and state a testable hypothesis about how the new algorithm will affect user behavior.

2. Design Experiment

Choose randomization unit (user-level), split traffic into control (old algorithm) and treatment (new algorithm), and determine sample size via power analysis.

3. Select Metrics

Define primary success metric (e.g., CTR), secondary metrics (e.g., engagement time), and guardrail metrics (e.g., unsubscribe rate, app uninstalls).

4. Run Experiment and Monitor

Launch the experiment, monitor for technical issues, and ensure data quality; avoid peeking at results prematurely.

5. Analyze and Decide

After the predetermined duration, analyze results using statistical tests, check for novelty effects, and make a data-driven decision to launch, iterate, or abandon.

Key Points to Mention

  • Randomization unit and sample size calculation (power analysis)
  • Primary, secondary, and guardrail metrics
  • Control group and treatment group setup
  • Duration of experiment and novelty effects
  • Statistical significance and practical significance
  • Potential network effects and interference

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