Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta DS interview with a product analytics case about estimating fake accounts on the platform. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

How would you design an analytics approach to estimate the number or proportion of fake accounts on a large social platform? Walk through your data signals, sampling strategy, and how you'd validate the estimate.

Product Analytics & MetricsA/B Testing & ExperimentationData Modeling

Author's notes

I started with supervised classification which felt natural but the interviewer kept pushing on how you'd even get ground truth labels at scale.

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AI HintsAI Generated

Suggested Approach

Start by defining what constitutes a fake account and the target metric (count or proportion), then outline a multi-signal detection approach combining behavioral, network, and content features. Propose a sampling strategy that balances statistical rigor with operational feasibility, and describe validation using labeled data and sensitivity analyses.

Pro tip: Acknowledge that no single method is perfect and propose a triangulation approach, combining multiple estimates to bound the true value. Also, emphasize the importance of aligning with business definitions and avoiding over-engineering by starting with a simple, interpretable model.

1. Define Fake Accounts and Target Metric

Clarify what qualifies as a fake account (e.g., bots, duplicate accounts, compromised accounts) and whether the goal is to estimate the total number or proportion. Align with stakeholders on the operational definition and the desired precision.

2. Identify Data Signals

List potential signals from user behavior (e.g., posting frequency, login patterns), network (e.g., friend connections, IP addresses), and content (e.g., spammy text, image reuse). Consider both supervised and unsupervised features.

3. Design Sampling Strategy

Choose a sampling method (e.g., random, stratified, or cluster sampling) that ensures representativeness and efficiency. Account for platform scale by using stratified sampling based on activity levels or regions, and determine sample size for desired confidence intervals.

4. Build and Validate Model

Train a classifier (e.g., logistic regression, gradient boosting) on labeled data, using cross-validation. Validate with holdout sets and assess performance metrics (precision, recall, AUC). If labels are scarce, use semi-supervised or active learning.

5. Estimate and Validate Population Metric

Apply the model to the sample to estimate the proportion of fake accounts, then extrapolate to the population with confidence intervals. Validate via sensitivity analysis, comparing with alternative methods (e.g., capture-recapture, anomaly detection) and manual review of borderline cases.

Key Points to Mention

Define fake accounts clearly and consider multiple types (bots, duplicates, compromised).
Use a combination of behavioral, network, and content signals for detection.
Employ stratified sampling to handle platform heterogeneity and ensure representativeness.
Address label scarcity by using semi-supervised learning or manual labeling of a subset.
Validate estimates with confidence intervals and sensitivity analysis.
Triangulate with alternative methods like capture-recapture or anomaly detection to bound the estimate.

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