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AI Mule Account Network Detector

William Bergmann

Full-time · Job · Chicago

About the role

Our fraud losses aren't coming from lone bad actors anymore, they're coming from coordinated networks of mule accounts moving money through dozens of seemingly unrelated profiles. Rules-based detection catches the obvious ones and misses the rest. This role exists to close that gap. You'll build graph-based and behavioral models that identify mule account networks by looking at transaction patterns, shared device fingerprints, and account creation velocity across seemingly unconnected accounts, feeding directly into our real-time fraud blocking pipeline. You'll work with our fraud operations team to validate detections against confirmed cases and continuously retrain models as fraud networks adapt their behavior.

Responsibilities

  • Build graph and behavioral models to detect coordinated mule account networks
  • Own the Spark-based feature pipeline feeding real-time fraud scoring
  • Validate detections against confirmed fraud cases with the operations team
  • Retrain and monitor models as fraud patterns evolve

Requirements

  • 5+ years in fraud detection, risk modeling, or applied ML
  • Strong scikit-learn and Spark experience at production scale
  • SQL fluency for large transactional datasets
  • Experience with graph-based or network analysis techniques

Benefits

  • Fully remote position
  • Competitive salary plus equity
  • Health and dental insurance
  • Security-conscious, low-bureaucracy engineering culture

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python spark sql scikit-learn

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Language

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Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python spark sql scikit-learn

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