Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Meta data science interview with a meaty ML evaluation question. The whole thing was pretty focused on one scenario, which sounds simple but the depth they expect is real.

Questions Asked (1)

A new ML model has been built to flag harmful content. How would you evaluate whether it actually performs better than the current system? Walk through both offline metrics and an online experiment design, and explain how you'd think about the precision-recall trade-off.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This is the kind of question where you can go in a lot of directions and that's kind of the problem.

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AI HintsAI Generated

Suggested Approach

Start by defining clear evaluation criteria and metrics that align with the product goal of reducing harmful content while minimizing false positives. Then, describe offline evaluation using a held-out test set with precision, recall, and PR-AUC, followed by an online A/B test design with guardrail metrics. Finally, discuss the precision-recall trade-off in terms of business impact and user experience.

Pro tip: Emphasize that offline metrics are necessary but not sufficient; the online experiment must account for user behavior changes and feedback loops, and consider using interleaving or switchback tests if randomization is challenging.

1. Define success metrics and constraints

Identify primary metrics (e.g., prevalence of harmful content, user reports) and guardrail metrics (e.g., false positive rate, user engagement). Clarify the acceptable trade-off between precision and recall based on product policy.

2. Offline evaluation

Use a labeled dataset to compute precision, recall, F1, and PR-AUC for both models. Analyze performance across subgroups and thresholds to understand trade-offs and potential biases.

3. Design online experiment

Propose an A/B test with random assignment, ensuring sufficient power. Define exposure, treatment, and control groups, and specify primary and guardrail metrics. Consider novelty effects and long-term holdout.

4. Analyze results and trade-offs

Compare models on online metrics, checking statistical significance and practical significance. Evaluate precision-recall trade-off by examining the impact on user experience and harmful content prevalence.

5. Recommend and iterate

Based on results, recommend whether to launch, iterate, or abandon. Suggest threshold tuning or model improvements to better balance precision and recall.

Key Points to Mention

Precision-recall trade-off: higher recall reduces harmful content but may increase false positives, impacting user experience.
Offline metrics: precision, recall, F1, PR-AUC, and calibration; use of confusion matrix at different thresholds.
Online experiment: A/B test design, randomization unit, sample size calculation, guardrail metrics (e.g., user reports, engagement).
Potential biases: evaluate model fairness across different user groups and content types.
Business impact: align metrics with company goals, such as reducing harmful content without stifling free expression.
Iterative process: use experiment results to refine model and thresholds, and consider long-term effects.

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