Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta DS interview focused entirely on content moderation analytics, specifically around report validity and detecting bad actors who spam the reporting system. Two SQL-heavy questions, both with a product reasoning layer underneath.

Questions Asked (2)

What share of reported users have received at least one valid report? Walk through how you'd define the numerator and denominator and handle any deduplication.

Product Analytics & MetricsData Modeling

Author's notes

I jumped straight into writing SQL before fully locking down the definitions, which was a mistake.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the business context and definitions first, then define the numerator as unique users with at least one valid report and the denominator as unique users with at least one report (or all reported users, depending on the question). Deduplicate at the user level and consider time windows and validity criteria.

Pro tip: Always state your assumptions about what 'reported user' and 'valid report' mean, and mention that the metric can be segmented by report type or user cohort to provide actionable insights.

1. Clarify definitions and scope

Ask clarifying questions to define 'reported user', 'valid report', and the time period. Confirm whether the denominator is all users who filed a report or all users who received a report.

2. Define numerator and denominator

Numerator: count of distinct users who have at least one valid report. Denominator: count of distinct users who have at least one report (or all reported users, based on scope).

3. Handle deduplication

Deduplicate at the user level using user IDs. For the numerator, ensure a user is counted once even if they have multiple valid reports. For the denominator, count each user once regardless of number of reports.

4. Apply validity criteria

Filter reports to only those marked as valid (e.g., not spam, not duplicate, meets policy). Apply this filter before deduplication for the numerator.

5. Consider time windows and edge cases

Decide on a time window (e.g., last 30 days) and handle edge cases like users with no reports, deleted accounts, or reports from the same user on the same content.

Key Points to Mention

Definition of 'valid report' (e.g., confirmed violation, not spam)
User-level deduplication using unique user IDs
Time window for the analysis (e.g., daily, weekly, monthly)
Distinction between reports received vs. reports filed
Handling of multiple reports per user or per content item
Potential segmentation by report type, user demographics, or content type

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

How would you identify users who are abusing the reporting feature by filing false reports? Describe the metrics you'd use and write SQL to surface them.

Product Analytics & MetricsRoot Cause Analysis

Author's notes

This is where it got interesting.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by defining what constitutes abuse—intentionally filing false reports—and translate that into measurable behavioral signals. Then outline a multi-metric detection strategy combining volume, accuracy, and network patterns, and finally write SQL that surfaces users with anomalous reporting behavior.

Pro tip: Acknowledge the precision-recall tradeoff: false positives (flagging legitimate reporters) can be costly, so propose a tiered review system where high-confidence abusers are auto-actioned and borderline cases go to human review.

1. Define abuse and success criteria

Clarify that abuse means intentionally filing false reports, and define what a 'false report' is (e.g., report dismissed by moderators). Establish that the goal is to identify likely abusers with high precision.

2. Identify behavioral signals and metrics

List metrics such as report volume, report dismissal rate, reporter-to-reported-user ratio, time between reports, and network overlap with other abusers. Consider both individual and graph-based signals.

3. Design detection logic and thresholds

Combine metrics into a scoring system or rules (e.g., >90% dismissal rate and >50 reports/week). Use statistical methods like z-scores or isolation forests to flag outliers.

4. Write SQL to surface candidates

Construct a query that aggregates report data per user, computes key metrics, and filters for suspicious patterns. Include joins to moderation outcomes and possibly self-joins for network analysis.

5. Validate and iterate

Propose validation via manual review of top candidates, A/B testing enforcement actions, and monitoring for false positives. Emphasize continuous improvement of the model.

Key Points to Mention

Define false reports using moderation outcomes (e.g., reports marked as invalid or dismissed).
Use a combination of volume metrics (reports per day) and quality metrics (dismissal rate).
Consider network effects: abusers may coordinate, so analyze shared targets or reporting rings.
Account for base rates and seasonality (e.g., more reports during events) to avoid false positives.
Write SQL with window functions (e.g., ROW_NUMBER, LAG) to detect bursts or changes in behavior.
Mention the importance of precision over recall to avoid penalizing legitimate users.

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