Sr. Data Science Manager - Financial Crimes

Location

4910 Tiedeman Road, Brooklyn Ohio

Reporting to the Director of Financial Crimes Modeling & Analytics that reports to the Deputy Chief AML Officer, the Senior Data Science Manager is primarily responsible for leading a quantitative team in support of financial crime modeling and analytics. This role must establish priorities for the team, assign and define the work to the right resources, and understand the strengths and opportunities of the team to ensure the team meets and exceeds expectations.

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 Senior Data Science Manager acts as a leader, strategic advisor and credible thought partner to senior / executive level business partners. This position is also responsible for managing the integration of activities within and across teams as well as effectively manage, coach, develop, and guide a team of professionals to anticipate business needs, recommend solutions that are both effective and efficient, and communicate the right message.

• Oversee a small quantitative team (2-4 direct reports) in developing models and conducting 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
• Employ innovative techniques to drive continuous improvements in model effectiveness and efficiency, e.g. reducing false positives
• Proactively coach and develop the team on building technical skills and business knowledge; promote the team and their capabilities to stakeholders
• 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 / executive 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 5 years of relevant experience, preferred in AML/BSA, OFAC, or fraud modeling/analytics
• Demonstrated proficiency in both traditional modeling practices and Machine Learning (ML)/Artificial Intelligence (AI) solutions, with preference given to candidates possessing experience and expertise in Generative AI (GenAI).
• 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
• Demonstrated direct management experience
• Excellent verbal, written and visual communication skills; ability to define and communicate key risk metrics, dashboards and analytics for senior management and executive reporting; translate technical observations to a non‑technical audience
• A credible thought leader that can guide, advise, challenge, and influence at all levels of the organization and drive organizational impact
• Remote work is supported, but relocation to Cleveland, OH or Buffalo, NY is preferred.

• Ability to:
  • Influence / impact strategy for data sourcing; can anticipate emerging needs to be incorporated into strategy
  • Lead discussions about pros/cons of applications with senior leadership
  • Monitor industry trends and direction of data analysis technologies

• Advanced Microsoft Office Suite
• SQL/No
  
  SQL
  • Relationship data structure
  • Selecting and retrieving data including unstructured data retrieval, archival, and ETL
  • Databases
• Advanced Python/R/SAS
  • Databases
  • Efficient coding
  • Can build strong code controls and translate code into high‑level commentary
• Understanding of and ability to leverage:
  • Cloud‑based computing
  • Distributed computing