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

Navy Federal Credit Union•Pensacola, FL

2d•$115,600 - $181,200•Onsite

About The Position

Navy Federal's Internal Audit team is undergoing a significant transformation to become a top-tier Audit function. Our vision is to be a trusted advisor to the business by consistently delivering high-quality, risk-focused audit and advisory work. We are committed to implementing efficient processes, maximizing technology utilization, integrating data analytics into all aspects of our operations, and investing in our people. If you are interested in joining a team with this focus, we encourage you to apply. This role provides independent assurance over data science, machine learning, generative AI, and agentic AI capabilities through audit engagements and technical reviews. The position involves evaluating model and AI governance frameworks, lifecycle controls, and oversight effectiveness, including the risks associated with model complexity, uncertainty, and operational performance. The Senior Quantitative Risk Analyst will lead quantitative analyses to inform risk management activities and serve as a subject matter expert in model design, data-driven experimentation, and scalable analytical solutions. Additionally, the role will advise Internal Audit staff, senior management, and business partners on AI/model lifecycle management, data governance, regulatory expectations, and industry best practices. The position requires conducting and managing increasingly complex projects with moderate supervision and independent judgment.

Requirements

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.

Nice To Haves

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.
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.

Responsibilities

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.