#JR-24039446
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Job Description:
This job is responsible for conducting quantitative analytics and complex modeling projects for specific business units or risk types. Key responsibilities include leading the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.
Enterprise Model Risk Management seeks a Sr Quantitative Fin Analyst - Anti-Money Laundering (AML) to conduct independent testing and review of complex models used to monitor and mitigate money laundering risk. The candidate should exhibit familiarity with industry practices and have knowledge of up-to-date AML techniques. The candidate should be able to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in applying statistical and machine learning methods to manage the bank's AML models and model systems.
The position will be responsible for:
Performing independent model validation, annual model review, ongoing monitoring report review, required action item review, and peer review.
Conducting governance activities such as model identification, model approval and breach remediation reviews.
Providing hands-on leadership for projects pertaining to statistical modeling and machine learning approaches to effectively challenge and influence the strategic direction and tactical approaches of these projects.
Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; communicating and interacting with the third line of defense (e.g., internal audit) as well as external regulators, as needed.
Writing technical reports for distribution and presentation to model developers, senior management, audit and banking regulators.
Acts as a senior level resource or resident expert on analytic/quantitative modeling techniques used for Anti-money laundering.
Minimum Education Requirement: PhD or Masters in a quantitative field such as Mathematics, Physics, Finance, Economics, Engineering, Computer Science, Statistics.
Required Qualifications:
Knowledge and 4+ years of experience in building and understanding of Anti-Money Laundering models and systems.
Strong familiarity with industry practices in the field and knowledge of up-to-date Anti-Money Laundering techniques
CAMS certification (preferred)
Fluency in Python, SAS and SQL
Excellent written and oral communication skills with stakeholders of varying analytic skills and knowledge levels.
Skills:
Critical Thinking
Quantitative Development
Risk Analytics
Risk Modeling
Technical Documentation
Adaptability
Collaboration
Problem Solving
Risk Management
Test Engineering
Data Modeling
Data and Trend Analysis
Process Performance Measurement
Research
Written Communications
Shift:
1st shift (United States of America)
Hours Per Week:
40
Learn more about this role