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AI Payment Fraud Network Graph Analyst

Cole Mueller

Full-time · Job · Chicago

About the role

Individual transaction fraud models are good at catching individual fraudsters. They're bad at catching fraud rings — coordinated networks where individual transactions look legitimate but the network pattern reveals the scheme. A ring of 300 synthetic identities sharing address components, device fingerprints, and referral codes is nearly invisible to per-transaction models. A graph neural network sees it immediately. We process $8B in payment volume per month and lose $14M annually to organized fraud rings that our current models miss. We've built the graph: 12 million nodes (accounts, devices, addresses, cards, merchants) and 90 million edges updated in near-real-time. We haven't built the intelligence layer that turns that graph into fraud signals. You'll own this capability. Design the graph ML models, build the feature engineering on top of the payment network graph, integrate the fraud signals into our existing decision engine, and define the monitoring that tracks ring detection accuracy as fraudsters adapt. This is high-stakes work: every percentage point improvement in ring detection precision saves roughly $140K per month.

Responsibilities

  • Design graph neural network models for payment fraud ring detection
  • Build graph feature engineering from the 12M-node, 90M-edge payment network
  • Integrate ring detection signals into the existing real-time decision engine
  • Develop adversarial robustness — fraudsters adapt, models must adapt faster
  • Build monitoring that tracks ring detection accuracy against confirmed fraud cases
  • Collaborate with the fraud operations team to validate newly detected ring patterns

Requirements

  • 6+ years in ML engineering with fraud detection or financial crime experience
  • Hands-on experience with graph neural networks in production
  • Strong Python, Spark, and SQL skills for large-scale graph data processing
  • Deep understanding of payment network structures and fraud patterns
  • Track record delivering measurable fraud loss reduction

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Equity participation
  • Bonus tied to fraud loss reduction metrics

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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

$130k – $160k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql statistics pandas

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