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Measure fake account prevalence

Last updated: Apr 2, 2026

Quick Overview

This question evaluates a data scientist's competency in fraud measurement, statistical estimation, experimental design, and model evaluation for detection systems, including consideration of class imbalance, delayed labels, selection bias, false-positive harm, and adaptive attacker behavior.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Measure fake account prevalence

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

A social platform is concerned about fake accounts. Leadership wants to understand how serious the problem is and whether a new detection model or enforcement policy should be launched. How would you approach this problem? In your answer, discuss: - how you would define a fake account operationally - how you would estimate prevalence and business impact - what metrics you would use to evaluate a detection system - how you would deal with class imbalance, delayed labels, and selection bias from user reports or manual reviews - how you would measure false-positive harm to legitimate users - how you would evaluate a new intervention online, given that attackers may adapt over time

Quick Answer: This question evaluates a data scientist's competency in fraud measurement, statistical estimation, experimental design, and model evaluation for detection systems, including consideration of class imbalance, delayed labels, selection bias, false-positive harm, and adaptive attacker behavior.

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Meta
Feb 22, 2026, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
3
0
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A social platform is concerned about fake accounts. Leadership wants to understand how serious the problem is and whether a new detection model or enforcement policy should be launched.

How would you approach this problem?

In your answer, discuss:

  • how you would define a fake account operationally
  • how you would estimate prevalence and business impact
  • what metrics you would use to evaluate a detection system
  • how you would deal with class imbalance, delayed labels, and selection bias from user reports or manual reviews
  • how you would measure false-positive harm to legitimate users
  • how you would evaluate a new intervention online, given that attackers may adapt over time

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