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. Responsibilities 3Leads IA's assessment of the design, development, validation, and implementation of advanced quantitative models, analytics strategies and AI products. 3Ability to evaluate approaches to assess model performance, scalability, robustness, bias, sensitivity, and risk under varying assumptions or operating conditions across model and AI products. 3Partners with cross functional teams to assess management's alignment of quantitative solutions with business objectives, technical feasibility, and implementation priorities. 3Mentors junior team members on modeling techniques, coding practices, documentation standards, and analytical storytelling. 3Develops and advances reusable frameworks, technical standards, and best practices that improve the quality and efficiency of IA quantitative analytics work. 3Communicates complex concepts, tradeoffs, and recommendations to senior stakeholders in a clear, practical, and decision-oriented manner. 3Identifies high value analytical opportunities and defines appropriate methodologies to address complex, ambiguous problems. Qualifications 3Bachelor'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. 35+ years of experience in analytics, mathematics, statistics, computer science, data science, operations research, economics, finance. 3Complete knowledge and understanding of business area/specialization. 3Advanced knowledge of quantitative modeling, optimization, statistical inference, machine learning, simulation, or algorithm design. 3Strong programming skills and experience building production quality analytical tools, data pipelines, models, or decision support systems. 3Demonstrated ability to lead complex analyses from problem definition through implementation and stakeholder adoption. 3Strong communication, influence, and consultative problem-solving skills. Desired Qualifications: 3Prior experience independently assessing and challenging model risk management, data governance and AI/Agentic governance within risk management or internal audit environments. 3Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc. 3Deep 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. 3Knowledge of one or more regulations and frameworks such as CECL, CCAR, BSA/Anti-Money Laundering, ECOA, FCRA, etc. 3Understanding of various models and modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, marketing models, etc. 3Programming, data modeling, simulation, and advanced mathematics 3Familiarity with coding languages such as SQL, R, Python, Hadoop, or SAS. 3Knowledge 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. 3Master's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or another quantitative or related field. Additional Information Hours: 3Monday - Friday, 8:00AM - 4:30PM Locations: 3820 Follin Ln. Vienna, Virginia 22180 3141 Security Drive Winchester, VA 22602 35550 Heritage Oaks Dr. Pensacola, FL 32526