Senior Data Scientist (Internal Audit Model & AI Risk)

Navy Federal Credit Union; Winchester (VA); On-site; USD 99,000 - 156,000; Full time

Job summary

Navy Federal Credit Union in Winchester, VA is seeking a Senior Data Scientist to support independent assurance by evaluating model and AI risk across the organization.

This onsite role focuses on audit and technical reviews of AI capabilities, including ML, generative AI, and agentic AI, combining model risk knowledge with technical testing for stakeholder communication throughout the audit lifecycle.

Qualifications

• 5-7 years of experience in data analysis or statistics with language models/generative AI.
• Strong knowledge of statistics, programming, data modeling.
• Proficiency in SQL, R, Python; experience with Hadoop/SAS/SPSS/Scala a plus.
• Familiarity with AI platforms like Copilot Studio, Azure AI Foundry, AWS, Databricks.
• Bachelor's degree in a quantitative field.

Responsibilities

• Assess AI and model governance, including risk ratings and monitoring.
• Lead internal audit reviews of AI products and models.
• Develop model testing plans and ensure governance compliance.
• Extract insights from large data sources to inform audits.
• Communicate findings to stakeholders with clear documentation.
• Code software tools to support audit analytics.

Skills

SQL

R

Python

Education

Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a similar quantitative field

Tools

Microsoft Copilot Studio

Azure AI Foundry

AWS

Databricks

Job description

Navy Federal Credit Union is advancing its Internal Audit function through a transformation focused on risk-focused audit and advisory work, greater use of technology, and deeper integration of data analytics. Within Internal Audit, this Senior Data Scientist role supports independent assurance by evaluating model and AI risk, governance frameworks, and lifecycle controls across the organization.

This onsite position in Winchester, VA 22602 centers on audit and technical reviews of complex data science and AI capabilities, including machine learning, generative AI, and agentic AI. The work combines model risk knowledge, technical testing, and clear communication to inform stakeholders across the audit engagement lifecycle.

Key Responsibilities

• Assess AI and model governance, including inherent risk ratings, model validations, materiality of model changes, and ongoing performance monitoring, using targeted model review approaches as part of governance audits.
• Execute and manage independent internal audit reviews of AI products, AI-enabled systems, and models, applying expertise in AI technologies, model risk, and governance practices.
• Follow established audit frameworks and assurance processes to deliver high-quality reviews while managing scope, timelines, stakeholder expectations, and communication.
• Develop, implement, and run model testing plans that require expert model risk knowledge and skills.
• Evaluate model development, implementation, and use across conceptual soundness, assumptions, methodology, limitations, data quality, ongoing monitoring, and additional complex lifecycle elements.
• Extract meaningful insights from large data and metadata sources, test hypotheses, analyze results, and interpret findings.
• Apply discretion and sound judgment within defined procedures and practices.
• Develop and code moderately complex software programs, algorithms, and automated processes to support analysis.
• Use modeling and trend analysis to analyze data and provide actionable insights.
• Support ethical AI best practices and contribute to documentation and presentations for diverse stakeholder audiences.
• Transform data into charts and tables to improve decision-making support.
• Build working relationships with team members and subject matter experts; lead small projects and initiatives.
• Provide independent assurance by assessing complex data science, machine learning, generative AI, and agentic AI capabilities through audit engagements and other technical reviews.
• Evaluate relevant ecosystems by reviewing governance frameworks, risk management practices, AI/model lifecycle controls, first- and second-line oversight effectiveness, and associated complexity, uncertainty, transparency, and operational risks.
• Serve as a subject matter expert to Internal Audit staff, senior management, and business partners on end-to-end AI/model lifecycle management, regulatory expectations, and industry practices.
• Conduct and manage work assignments of increasing complexity under moderate supervision with some latitude for independent judgment.

Required Qualifications

• 5-7 years of experience in data analysis, statistical modeling, or regression analysis, including experience with language models, LLMs, and/or generative AI technologies.
• Basic understanding of business and operating environment.
• Statistics; programming, data modeling, simulation, and advanced mathematics.
• SQL, R, Python and experience with one or more: Hadoop, SAS, SPSS, Scala.
• Knowledge of AI platforms and ecosystems supporting machine learning, generative AI, and LLM-enabled systems, including Microsoft Copilot Studio, Azure AI Foundry, AWS, and Databricks.
• Experience with model lifecycle execution including model development, validation, and governance.
• Technical writing, data storytelling, and technical presentation skills.
• Research skills, interpersonal skills, communication, and critical thinking.
• Working knowledge of procedures, instructions, and validation techniques.
• Collaboration and relationship building; initiative with sound judgment; problem solving.
• Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a similar quantitative field.

Desired Qualifications

• Prior experience with model validation/model risk management, AI and agentic risk management, enterprise risk management, or internal audit.
• Deep knowledge and experience with SR 11-7 and/or ASOP 56.
• Understanding of modeling practices used in credit risk management, fraud detection, BSA/AML, operations, treasury & finance, and marketing models.
• Knowledge of one or more regulations and frameworks, such as CECL, CCAR, Anti-Money Laundering, ECOA, FCRA, NIST AI Risk Management Framework, and ISO/IEC 42001 (Artificial Intelligence Management System).
• Advanced knowledge of Navy Federal Credit Union instructions, standards, and procedures.
• Master's Degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, or Engineering.

Technologies

• SQL, R, Python, Hadoop, SAS, SPSS, Scala
• Microsoft Copilot Studio, Azure AI Foundry, AWS, Databricks
• Language models, LLMs, generative AI, agentic AI

Schedule: Monday - Friday, 8:00AM - 4:30PM
Salary: USD 99,400 - 155,850 per year
Location(s) listed: 141 Security Drive, Winchester, VA 22602 (onsite); 820 Follin Lane, Vienna, VA 22180; 5510 Heritage Oaks Drive, Pensacola, FL 32526