About this role
Manager, AML/ATF Screening Models and Analytics, Global AML
The Manager, AML/ATF Screening Models and Analytics is responsible for applying sophisticated predictive/analytical modelling techniques and latest AML/ATF rules and scenarios to new models and for enhancing the performance of existing Client and Payment Screening models. This role contributes to the overall success of the Global Compliance and AML team by ensuring specific individual goals, plans, and initiatives are delivered in support of the team’s business strategies and objectives. Also ensures all activities conducted follow governing regulations and internal policies and procedures.
Roles and responsibilities include: Lead data extraction, transformation, and advanced analysis using Python (pandas, numpy, ML libraries) and SQL to support scalable analytics and model development. Design, develop, test, tune, and implement statistical and machine‑learning models, including AML/ATF client and payment screening rules and models. Conduct in‑depth data profiling and analysis of client and payment source data to identify data quality gaps, enrichment opportunities, and predictive modeling enhancements. Evaluate the effectiveness of screening systems and models, identifying risks, gaps, and performance issues, and provide clear, prioritized recommendations for improvement. Identify and demonstrate measurable value in opportunities to improve model efficiency, effectiveness, and alert quality through analytical and modeling enhancements. Translate complex analytical and modeling outputs into clear, actionable insights for both technical and non‑technical stakeholders. Produce high‑quality model documentation to support model development, validation, governance, and audit requirements. Properly identify, report, and escalate issues that may impact the AML/ATF program resulting from model performance, system functionality, data issues, or application changes. Apply hands‑on experience with AML/ATF screening pl