Manager II, Data Science; Audit Product Engineering

Position: Manager II, Data Science (Audit Product Engineering)
Overview

Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.

Navy Federal's Internal Audit team is in the midst of an exciting transformational journey to become a best-in-class Audit function! It is our vision to be a preferred advisor to the business by building and cultivating trust through the consistent execution of high-quality and risk-focused audit and advisory work. We're focused on implementing efficient processes, maximizing our use of technology, integrating data analytics into everything we do, and investing in our biggest asset, our people.

If this sounds like the type of team you'd like to be a part of, then we want to learn more about you!

Internal Audit's Manager II, Data Science (Product Engineering) is responsible for leading a team of data analytics, data management and AI specialists focused on developing and maintaining data products to drive Internal Audit's efficiency and effectiveness. Establish continuous monitoring and continuous auditing capabilities to dynamically guide audit coverage across the organization. Implement development operations best practices, from intake and requirements gathering through user acceptance testing and release management.

Align product development with Internal Audit and Navy Federal's organizational priorities. Serve as a subject matter expert for the use of predictive models, AI, and Agentic AI to reduce manual effort and increase insights throughout the audit lifecycle, from pre-planning through fieldwork and reporting.

This position is eligible for the Talent Quest employee referral program. If an employee referred you for this job, please apply using the system-generated link that was sent to you.

Responsibilities

• Lead and manage a team of technical staff responsible for developing reusable data products such as dashboards, automations and Agentic AI capabilities to increase audit efficiency and effectiveness.
• Maintain Internal Audit's data product backlog, including the intake, refinement, and prioritization of development in alignment with Internal Audit's organizational priorities.
• Oversee the use of automation to reduce manual effort across the audit lifecycle, from pre-planning and planning through fieldwork, issues management and reporting while ensuring audit work meets quality standards, is well-supported, and adheres to professional audit guidance (e.g., IIA Standards).
• Partner with senior Internal Audit leadership and cross-functional teams (e.g., Model Risk Management, Enterprise Data Risk Governance) to implement appropriate frameworks and controls to manage AI and Agentic AI risk.
• Provide expert review of technical work products including code, requirements documentation, UAT results and project plans to ensure accuracy, completeness, and reliability of data products
• Drive the evolution of our data science practices by implementing best practices in model development, validation, and deployment across various business units
• Collaborate with senior leadership and cross-functional teams to define strategic objectives, establish key performance indicators (KPIs), and prioritize projects that deliver actionable insights to the organization
• Communicate complex data-driven insights in a clear and concise manner to diverse audiences, fostering understanding and buy-in from stakeholders at all levels
• Manage multiple teams and/or specialized units to include resource planning, ensuring the execution of complex modeling initiatives; partner with leaders in analytics, engineering, and business domains to integrate models into workflows
• Analyze large complex datasets to extract actionable insights and present findings to stakeholders, using effective visualization and storytelling techniques
• Review complex code and analyses to ensure accuracy, scalability, and ethical alignment
• Ensure the continuous improvement of data quality and governance processes by collaborating with data engineering and IT teams
• Ensure appropriate use of advanced statistical techniques and manage model lifecycle governance
• Develop and…