Responsibilities
Develop and optimize AML risk models (rule-based engines + machine learning)
Design transaction monitoring strategies (e.g., anomaly detection, transaction structuring, fund flow analysis)
Analyze on-chain data to track fund movements and identify high-risk address behaviors
Build user risk scoring systems integrating KYC, transactional, and blockchain data
Continuously improve model performance, reduce false positives, and enhance investigation efficiency
Requirements
5+ years of experience in AML / risk modeling ( mandatory )
Proven end-to-end experience in deploying AML models (e.g., transaction monitoring, risk scoring, investigation support)
Strong proficiency in Python and SQL for data analysis and modeling
Solid understanding of common AML typologies (e.g., layering, smurfing, cash-out)
Hands-on experience with machine learning models (e.g., XGBoost, LightGBM)
Preferred Qualifications
Experience in crypto / blockchain analytics
Familiarity with on-chain analytics tools (e.g., Chainalysis, Elliptic)
Background in AML within payments, banking, or crypto industries
Experience with graph analytics or real-time risk systems
Goals
Enhance detection capabilities for complex money laundering activities
Reduce false positives and improve investigation efficiency
Proactively identify high-risk fund flows