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Coins.ph

Senior AML Modeling Engineer (Payments & Crypto)

Reposted 18 Days Ago
Be an Early Applicant
Remote or Hybrid
Hiring Remotely in Singapore, SGP
Senior level
Remote or Hybrid
Hiring Remotely in Singapore, SGP
Senior level
Develop and optimize AML risk models, design transaction monitoring strategies, analyze on-chain data, and build user risk scoring systems to enhance compliance capabilities.
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Job Description

We are looking for an experienced AML Modeling Expert to build and enhance anti-money laundering models across fiat payment and crypto trading scenarios. This role will leverage on-chain and transactional data to identify complex money laundering patterns and strengthen the platform’s overall risk control and compliance capabilities.

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

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