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Senior Quantitative Risk Analyst – Internal Audit, Model & AI Governance
Navy Federal Credit Union. Provide independent assurance over data science, machine learning, generative AI, and agentic AI capabilities through audit engagements and technical reviews .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in quantitative modeling, machine learning, and data governance, with a strong ability to lead complex analyses and communicate findings effectively to stakeholders. Proficient in developing analytical frameworks and mentoring team members in modeling techniques and coding practices.
Highest-signal resume keywords
Quantitative ModelingMachine LearningData GovernanceProgramming SkillsModel Risk Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Quantitative ModelingStatistical InferenceOptimizationAlgorithm DesignData ModelingSimulationAdvanced MathematicsAnalytical Tools DevelopmentDecision Support SystemsData Pipelines
Soft Skills
Strong CommunicationInfluenceConsultative Problem-SolvingMentoringStakeholder Engagement
Tools & Technologies
SQLRPythonHadoopSASMicrosoft Copilot StudioAzure AI FoundryAWSDatabricksPowerBI
Industry Keywords
Model Risk Regulatory GuidanceNIST AI Risk Management FrameworkISO/IEC 42001CECLCCARBSA/Anti-Money LaunderingECOAFCRACredit RiskFraud Detection
Tech Stack
Tools & technologiesAWSAzureHadoopPythonSQL
About the role
Key responsibilities & impact- 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
- Assess risks associated with model complexity, uncertainty, and operational performance
- Lead quantitative analyses informing risk management activities
- 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
- Lead Internal Audit assessments of the design, development, validation, and implementation of quantitative models, analytics strategies, and AI products
- Evaluate model and AI product performance, scalability, robustness, bias, sensitivity, and risk
- Partner with cross-functional teams to assess alignment with business objectives, technical feasibility, and implementation priorities
- Mentor junior team members on modeling techniques, coding practices, documentation standards, and analytical storytelling
- Develop reusable frameworks, technical standards, and best practices for Internal Audit quantitative analytics
- Communicate complex concepts, tradeoffs, and recommendations to senior stakeholders
- Identify high-value analytical opportunities and methodologies for complex, ambiguous problems
- Conduct and manage increasingly complex projects with moderate supervision and independent judgment
Requirements
What you’ll need- Bachelor’s degree in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance or equivalent combination of education, training or experience
- 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
- Ability to lead complex analyses from problem definition through implementation and stakeholder adoption
- Strong communication, influence, and consultative problem-solving skills
- Prior experience assessing and challenging model risk management, data governance, and AI/Agentic governance
- Understanding of models and modeling practices used in credit risk, fraud detection, BSA/AML, operations, treasury and finance, and marketing
- Knowledge of model risk regulatory guidance, including SR 11-7, SR 26-2, and ASOP 56
- Knowledge of AI frameworks including NIST AI Risk Management Framework and ISO/IEC 42001
- Knowledge of regulations and frameworks such as CECL, CCAR, BSA/Anti-Money Laundering, ECOA, and FCRA
- Programming, data modeling, simulation, and advanced mathematics
- Familiarity with SQL, R, Python, Hadoop, or SAS
- Knowledge of AI platforms and data ecosystems, including Microsoft Copilot Studio, Azure AI Foundry, AWS, Databricks, and PowerBI
- Must be authorized to work in the United States without current or future sponsorship
Benefits
Comp & perks- Highly competitive pay
- Generous benefits and perks
- Meaningful career experience
- Energized, engaged and committed culture
- Employee referral program eligibility