Design measurement to detect fake accounts
Overview
This question evaluates a data scientist's competencies in fraud detection measurement, event instrumentation, labeling strategy, metric design and reporting using constrained product signals.
Meta
Nov 16, 2025, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
0
0
Context
You work on a social platform. The only product surface you can rely on is friend requests (sending/receiving/accepting/declining). Assume you have no existing anti-fake model, no rules, and no established metrics.
Task
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Define “fake account” operationally.
- What behaviors qualify (spam, scam, bot, account farming)?
- How will you handle ambiguous/gray accounts?
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Design the data and instrumentation.
- What events and fields would you log for friend requests and subsequent user actions?
- What joins/identifiers are needed to track outcomes over time?
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Propose an initial detection approach without a model.
- What heuristic signals or risk scoring would you start with (rate limits, graph patterns, acceptance ratios, burstiness, messaging-after-accept if available, etc.)?
- How would you choose thresholds and prevent hurting legitimate users?
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Measurement & evaluation plan.
- How will you obtain labels (manual review, user reports, enforcement actions) and deal with delayed/biased labels?
- What are the primary , diagnostic , and guardrail metrics?
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Platform-level reporting.
- How would you estimate and report the platform’s fake-account problem over time (prevalence/incidence), given that you only observe partial ground truth?
- What would you show to an executive audience vs an operational team?
Solution
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