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AI Transaction Velocity Abuse Detector

William Bergmann

Full-time · Job · New_York

About the role

Velocity abuse is fraud in slow motion. A single account makes 3 transactions per day — nothing alarming. Zoom out and you see 8,000 accounts following the same pattern, all created within the same 72-hour window, all routing money through the same intermediary. The fraud is visible only at the graph level, and it happens fast enough that by the time a rules engine flags it, the money is gone. We build fraud detection infrastructure for payment networks and neobanks. Our velocity abuse detection system processes 280 million events per day, connecting individual transaction signals to account-level and network-level abuse patterns in real time. The current system catches 78% of velocity fraud rings before they cash out; the target is 90%. You'll redesign the ring detection layer — graph-based features from transaction networks, temporal pattern models that identify coordinated account behavior, and the scoring system that balances detection rate against false positives in a customer-facing context where blocking legitimate users is its own expensive problem.

Responsibilities

  • Redesign velocity abuse ring detection using graph-based transaction network features
  • Build temporal coordination models identifying synchronized account behavior patterns
  • Design false-positive management framework balancing detection and customer experience
  • Implement real-time scoring pipeline processing 280M daily events within SLA
  • Build evaluation framework using confirmed fraud ring case histories
  • Collaborate with risk operations team on alert investigation and model feedback

Requirements

  • 6+ years in fraud detection or ML engineering with financial crime domain experience
  • Experience with graph neural networks or graph-based feature engineering
  • Strong Python, Spark, and SQL skills for large-scale event processing
  • Deep understanding of precision-recall trade-offs in fraud detection contexts
  • Track record building real-time ML systems at high transaction volume

Benefits

  • Fully remote
  • Top-of-market compensation
  • Health, dental, and vision
  • $5,000 annual learning budget
  • Equity package

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql statistics

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql statistics aws

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