Assistant Vice President - Financial Crimes Risk Officer - Data Management

The Financial Crimes Program Advisory Assistant Vice President is a key member of the First Line Financial Crimes Risk Office Team, responsible for maintaining and enhancing the first line of defense financial crimes program. This includes defining risk mitigation strategies, policy development, and technology requirements for Wealth Management & E TRADE, and supporting the U.S. Banks and Investment Management businesses.

This role requires engagement with various partners to develop, enhance, and redesign financial crimes policies, processes, and procedural requirements. The focus is on risk mitigation, execution enhancement, automation, and control improvements. The AVP assists the team on strategic priorities to lead the development and implementation of new program requirements to ensure regulatory compliance and operational effectiveness. This position serves as a trusted advisor to the business, providing solutions to complex and high-impact issues.

Additionally, the role involves collecting and analyzing data to deliver actionable insights that support decision-making and strategic planning. It requires close collaboration with stakeholders to create scalable data science solutions aligned with regulatory requirements and industry best practices.

• Support the design and implementation of new processes, requirements, policy changes, and operating models for Wealth Management, E TRADE, U.S. Banks, and Investment Management.
• Evaluate and recommend enhancements to financial crimes controls, including AML, sanctions, and tax evasion measures.
• Document and assess end‑to‑end business processes within the Single Process Inventory (SPI), focusing on data attributes, sources, control mechanisms, and system interfaces.
• Ensure consistency in definitions and process rules across SPIs.
• Conduct data integration, profiling, and visualization using tools such as Excel, Dataiku, Python, Tableau, and others to support financial crimes strategy design.
• Research and analyze enterprise datasets to identify key data elements from Wealth Management and U.S. Banks systems to drive process improvements.
• Partner with technology teams to enhance data environments, integrating data tables required for analytical purposes.
• Monitor service and productivity levels, recommending and implementing continuous improvements.
• Participate on key initiatives aimed at assessing and mitigating financial crime and operational risk exposure.
• Contribute to multiple work streams with competing priorities in a dynamic, fast‑paced environment.
• Deliver clear, concise, and proactive communication through professional PowerPoint presentations tailored to various audiences.
• Develop innovative, forward‑thinking solutions to enhance controls, increase efficiency, and improve the customer experience.

• 5+ years of relevant experience with BSA/AML Regulations within the financial services industry, or at a financial services regulator (e.g., FINRA, Securities and Exchange Commission, Office of the Comptroller of the Currency, Federal Reserve Bank, etc.).
• Bachelor's degree in Business, Finance, or other related fields.
• Strong leadership qualities and evidence of a strong work ethic and high integrity.
• Implementation and execution of Financial Crimes Programs requirements.
• Knowledge of Financial Crimes laws and regulations and familiarity with best practice first line procedures and processes.
• Familiarity with data reconciliation across applications.
• Experience with data application mapping (inflows and outflows).
• Experience with relational databases, file systems, reading & writing code (e.g., SAS, Python, SQL).
• Experience in one or more of the following areas: business intelligence/analytics, data management, data governance, data controls, data quality management, and/or data architecture.
• Understanding of data governance, data quality, and data management concepts.
• Strong experience in Microsoft Office applications (Microsoft Excel/PowerPoint/Word/Access).
• In-depth knowledge and experience with various machine learning algorithms (supervised and unsupervised) and analytical toolkits.

• Analyze, summarize, and communicate…