• 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

• 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