This role is an individual contributor position within the Bank’s Enterprise Financial Crime Compliance (EFCC) organization. The person in this role will join a team of Financial Intelligence Unit (FIU) quantitative analysts responsible for the ongoing monitoring, testing, analytical review, and governance of AML Transaction Monitoring models. The individual will partner closely with model owners, Model Risk Management, internal and external auditors, and senior leadership to evaluate model behavior, identify emerging risks, and support enhancements to AML monitoring capabilities across the enterprise. This team is integral to maintaining and enhancing the Bank’s Anti-Money Laundering Transaction Monitoring systems. The ideal candidate will have experience evaluating quantitative models or monitoring frameworks in a regulated environment and a demonstrated ability to analyze complex data, investigate changes in model behavior, and develop well-supported conclusions. Candidates should possess a strong understanding of model risk concepts, performance monitoring, sampling methodologies, and root cause analysis. Proficiency in SAS, SQL, and Python for data analysis, testing, automation, and code review is highly desirable. The successful candidate will be able to assess whether code logic, data inputs, transformations, and outputs align with documented intent. Strong written and verbal communication skills are essential, as findings and recommendations must be effectively communicated to model owners, Model Risk Management, auditors, regulators, and senior leadership. This individual is expected to be highly self-driven, well-organized, and efficient in time management, capable of handling multiple tasks at once. Team responsibilities include performing ongoing monitoring and performance assessments of AML Transaction Monitoring models, conducting comprehensive code reviews and code change assessments, executing below-the-line testing of model thresholds, and performing detailed analytical research to understand model behavior changes, emerging trends, and the underlying drivers of observed performance shifts. Each task requires comprehensive, well‑structured documentation of analyses and conclusions such that work can be independently understood, reviewed, and defended without additional explanation.
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Job Type
Full-time
Career Level
Senior