AI Transaction Graph Anomaly Detector

About the role

Responsibilities

• Design graph structural features capturing entity relationship patterns correlated with fraud ring behavior
• Build subgraph anomaly detection identifying suspicious network motifs and unusual connectivity patterns
• Implement near-real-time graph scoring meeting latency requirements for transaction decision flows
• Develop ring detection models learning from confirmed fraud ring structures in historical data
• Create fraud signal integration feeding graph anomaly scores into the existing decision engine
• Build evaluation framework measuring ring detection precision against confirmed fraud investigation outcomes

Requirements

• 6+ years in ML engineering with graph analytics or financial fraud detection experience
• Strong Python, SQL, and Spark skills with graph data processing at scale
• Experience with graph neural networks or graph-based anomaly detection
• Deep understanding of payment network structures and fraud ring coordination patterns
• Track record delivering fraud detection improvements with measurable 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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