Lead Project Manager - Tookitaki AML Platform

About the Role

Lead the implementation and enhancement of Tookitaki's Anti-Money Laundering (AML) Suite for customer prioritization, alert disposition, and dynamic customer risk ranking. This role drives the adoption of AI/ML-powered AML solutions to improve detection accuracy and operational efficiency.

• Lead Tookitaki AML platform implementation projects for customer risk management
• Drive customer prioritization model deployment and refinement initiatives
• Manage alert disposition workflow optimization projects using Tookitaki's capabilities
• Oversee dynamic customer risk ranking implementation and integration
• Coordinate with data science teams on machine learning model deployment
• Manage end-to-end project delivery including requirements, design, build, and deployment
• Align Tookitaki solutions with AML risk framework and regulatory requirements
• Lead change management and user adoption for AI/ML-driven AML processes
• Establish KPIs and metrics to measure platform effectiveness
• Manage vendor relationship with Tookitaki and ensure SLA compliance
• Drive continuous improvement through model retraining and feature enhancement
• Produce executive dashboards and reports on AML program effectiveness

• Tookitaki Expertise:
  Hands-on experience with Tookitaki AML Suite (Anti-Money Laundering, Transaction Monitoring, Customer Risk Scoring)
• AI/ML in AML:
  Understanding of machine learning models for anomaly detection, risk scoring
• Project Management: 8+ years leading technology projects, preferably in Reg Tech/AML
• Customer Risk:
  Experience with dynamic customer risk rating methodologies
• Alert Management:
  Knowledge of alert disposition workflows and optimization
• Data Analytics:
  Familiarity with data science concepts, feature engineering, model validation
• Regulatory Compliance:
  Strong knowledge of MAS AML/CFT requirements
• Integration:
  Experience integrating AML platforms with core banking, KYC systems

• PMP, Agile, or similar project management certification
• CAMS, CFE, or equivalent AML/fraud certification
• Background in data science or analytics
• Experience with Python, R, or SQL for data analysis
• Knowledge of model risk management (MRM) frameworks
• Previous experience implementing AI/ML solutions in banking
• Familiarity with graph analytics and network analysis for AML