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AI Fake Review Network Detection Analyst

Ryan Kovacs

Contract · Job · New_York

About the role

We just signed two new enterprise retail clients who are losing revenue to coordinated fake review networks, and they need detection running before their next product launch cycle. Reliability and auditability are non-negotiable, since flagged reviews may end up in legal disputes with sellers. You will build network analysis models that detect coordinated fake review activity across seller accounts, using graph features and behavioral signals rather than simple keyword filters that bad actors easily evade. The impact is immediate: clients get a defensible detection system before their launch window, and every flagged network you catch protects real customers from being misled by manipulated ratings.

Responsibilities

  • Build graph-based models to detect fake review networks
  • Design behavioral signals that resist simple evasion
  • Document detection logic for legal defensibility
  • Deliver detection coverage ahead of client launch windows
  • Monitor false positive rates on legitimate sellers

Requirements

  • 3+ years working with fraud or network analysis
  • Experience with graph-based anomaly detection
  • Strong Python and SQL skills
  • Comfortable working under tight client deadlines

Benefits

  • Remote contract with potential extension
  • Competitive hourly or project rate
  • Flexible schedule
  • Direct access to client data science leads

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Job Type

Contract

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

data science & analytics

Skills

python sql scikit-learn statistics

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