Quant Analytics Senior Associate - Financial Crimes

Under Quantitative Analytics Sr. Manager's supervision, the Quantitative Analytics Sr. Associate is primarily responsible for conducting quantitative modeling and analytics of financial crimes. Leveraging both current and emerging technologies and applications, this role covers all key aspects of model development and analytics, i.e. data identification and gathering, methodology/technique selection, performance assessment, documentation, and ongoing monitoring.

• Under Quantitative Analytics Sr. Manager's oversight, 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; and effectively collaborate with compliance, technology, and risk partners

• Master's degree (or its equivalent) in statistics, mathematics, economics, computer science, data sciences, predictive modeling, or other quantitative disciplines and at least 3 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
• Knowledge of financial crime regulatory requirements, technology, and data analysis best practices
• Excellent verbal, written and visual communication skills; ability to translate technical observations to a non-technical audience

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