Senior Quantitative Risk Analyst (Internal Audit - Model & AI Governance)

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!

Provide independent assurance over data science, machine learning, generative AI, and agentic AI capabilities through audit engagements and technical reviews. Evaluate model, AI and data governance frameworks, lifecycle controls, and oversight effectiveness, including risks associated with model complexity, uncertainty, and operational performance. Lead quantitative analyses that inform risk management activities and serve as a subject matter expert in model design, data-driven experimentation, and scalable analytical solutions. Advise Internal Audit staff, senior management, and business partners on AI/model lifecycle management, data governance, regulatory expectations, and industry practices. Conduct and manage increasingly complex projects with moderate supervision and independent judgment.

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

• Leads IA’s assessment of the design, development, validation, and implementation of advanced quantitative models, analytics strategies and AI products.
• Ability to evaluate approaches to assess model performance, scalability, robustness, bias, sensitivity, and risk under varying assumptions or operating conditions across model and AI products.
• Partners with cross functional teams to assess management’s alignment of quantitative solutions with business objectives, technical feasibility, and implementation priorities.
• Mentors junior team members on modeling techniques, coding practices, documentation standards, and analytical storytelling.
• Develops and advances reusable frameworks, technical standards, and best practices that improve the quality and efficiency of IA quantitative analytics work.
• Communicates complex concepts, tradeoffs, and recommendations to senior stakeholders in a clear, practical, and decision-oriented manner.
• Identifies high value analytical opportunities and defines appropriate methodologies to address complex, ambiguous problems.

• Bachelor’s degree in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance or the equivalent combination of education, training or experience; Master's/advanced Degree preferred.
• 5+  years of experience in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance.
• Complete knowledge and understanding of business area/specialization.
• Advanced knowledge of quantitative modeling, optimization, statistical inference, machine learning, simulation, or algorithm design.
• Strong programming skills and experience building production quality analytical tools, data pipelines, models, or decision support systems.
• Demonstrated ability to lead complex analyses from problem definition through implementation and stakeholder adoption.
• Strong communication, influence, and consultative problem-solving skills.

Desired Qualifications: 

• Prior experience independently assessing and challenging model risk management, data governance and AI/Agentic governance within risk management or internal audit environments.

• Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc. 

• Deep knowledge and experience with model risk regulatory guidance; e.g., SR 11-7, SR 26-2, ASOP 56, as well as developing AI frameworks including NIST AI Risk Management Framework, and ISO/IEC 42001Artificial Intelligence Management System.

• Knowledge of one or more regulations and frameworks such as CECL, CCAR, BSA/Anti-Money Laundering, ECOA, FCRA, etc.

• Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc. 

• Programming, data modeling, simulation, and advanced mathematics

• Familiarity with coding languages such as SQL, R, Python, Hadoop, or SAS.

• Knowledge of AI platforms and data ecosystems supporting machine learning, generative AI, analytics, and LLM-enabled systems, including Microsoft Copilot Studio, Azure AI Foundry, AWS, Databricks and PowerBI.

• Master's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or another quantitative or related field.

Additional Information

Hours: 

• Monday - Friday, 8:00AM - 4:30PM

Locations: 

• 820 Follin Ln. Vienna, Virginia 22180
• 141 Security Drive Winchester, VA 22602
• 5550 Heritage Oaks Dr. Pensacola, FL 32526