ABOUT THE JOB
The Quantitative Analytics Manager is primarily responsible for conducting quantitative modeling and analytics of financial crimes, covering all key aspects, i.e. data identification and gathering, methodology/technique selection, performance assessment, documentation, and ongoing monitoring.
Leading critical projects, this role is responsible for owning modeling practices, methods, and techniques as well as for influencing data strategy with a focus on leveraging both current and emerging technologies and applications.
The Quantitative Analytics Manager acts as a leader, strategic advisor and credible thought partner to senior level business partners.
ESSENTIAL JOB FUNCTIONS
- Independently perform a broad range of quantitative works, including model development and ad hoc analytics to address financial crime compliance needs in AML/BSA/OFAC
- Research, compile and evaluate large sets of data to assess quality and determine suitability for model building
- Develop/maintain internal models and test/configure vendor solutions to ensure conceptually sound design, proper implementation, and acceptable model performance
- Document model development process and outcomes properly and support model validation and review
- Employ innovative techniques to drive continuous improvements in model effectiveness and efficiency, e.g. reducing false positives
- Proactively develop and build technical skills and business knowledge, coach and mentor junior members
- Effectively collaborate with compliance, technology, and risk partners; guides, advises, challenges, and influences to drive organizational impact
- Provide strategic consultation and thought leadership to senior level business partners
- Master's degree (or its equivalent) in statistics, mathematics, economics, computer science, data sciences, predictive modeling, or other quantitative disciplines and at least 4 years of relevant experience, preferred in AML/BSA, OFAC, or fraud modeling/analytics
- Solid expertise with both traditional and Machine Learning (ML)/Artificial Intelligence (AI) modeling practice and solutions
- Hands-on work experience with statistical coding in SAS and/or Python
- Knowledge of and ability to leverage traditional databases, cloud-based computing, and distribution computing
- In-depth understanding of financial crime regulatory requirements, technology, and data analysis best practices