Senior Director - Internal Audit - Data Science and AI

Senior Director – Internal Audit - Data Science and AI

Location: Reston, VA 20190 / Washington, DC / Remote within the United States
Work Arrangement: Hybrid for candidates local to Washington, DC or Reston, VA; remote consideration for eligible candidates residing elsewhere in the 50 U.S. states
Employment Type: Full-Time
Work Hours: Eastern Time business hours, 8:30 AM – 5:00 PM ET

The Senior Director – Internal Audit, Data Science and AI will lead the vision, strategy, development, and adoption of AI, data science, analytics, automation, and technology products across Internal Audit.

This is a hands-on technical leadership role. The successful candidate must be able to lead a small, highly technical team while personally contributing to AI model development, coding, model refinement, and technical architecture—not simply supervise technical professionals.

The role will drive AI-enabled transformation across the audit lifecycle, building scalable, governed, and reusable capabilities that improve risk detection, continuous monitoring, evidence generation, control-risk reporting, audit quality, and operational efficiency.

The leader will partner with Audit, Technology, Enterprise AI, Data Science, Risk, Compliance, and business stakeholders to deliver measurable outcomes while maintaining strong governance and responsible AI practices.

Key Responsibilities

AI Strategy and Product Leadership

• Develop and execute the Internal Audit AI, analytics, and automation roadmap aligned with audit priorities, Board objectives, and the enterprise AI vision.
• Own strategy, product roadmaps, delivery, production adoption, and performance measurement for AI, GenAI, analytics, and automation capabilities.
• Drive measurable improvements in audit efficiency, quality, risk detection, and insight generation.
• Establish reusable AI-enabled products and platform capabilities that reduce delivery time and increase adoption across the audit lifecycle.

Hands-On Technical Leadership

• Personally contribute to AI/ML model development, coding, predictive modeling, model refinement, and technical problem-solving.
• Guide data scientists and AI engineers in selecting appropriate modeling techniques, validating assumptions, calibrating models, and improving performance.
• Lead the architecture and development of advanced AI workflows, including retrieval-augmented generation (RAG), intelligent agents, evaluation suites, and orchestration.
• Evaluate production readiness, scalability, reliability, security, monitoring, and governance for AI and multi-agent workflows.
• Maintain technical credibility while leading and developing a small team of highly skilled professionals.

Enterprise AI Platform and Architecture

• Partner with Technology to design and scale a unified audit platform supporting governed data products, reusable capabilities, APIs, microservices, GenAI services, model operations, and secure delivery.
• Promote scalable software engineering, cloud architecture, data platform integration, and model lifecycle management practices.
• Enable reusable enterprise AI capabilities and extend successful solutions across the three lines of defense where appropriate.

Audit, Risk, and Responsible AI

• Enable continuous monitoring, anomaly detection, full-population testing, automated evidence generation, and control-risk reporting.
• Maintain strong data governance, model risk management, responsible AI, operational controls, and regulatory alignment.
• Partner with Risk, Compliance, Enterprise AI, and Audit stakeholders to manage technical and business risks.
• Ensure AI supports professional audit judgment while improving coverage, consistency, and insight.

Team and Executive Leadership

• Build, coach, and retain high-performing data science, AI engineering, product, and automation professionals.
• Establish operating models, talent plans, delivery measures, adoption goals, and performance metrics.
• Advise executives and VP-level stakeholders, challenge assumptions, and explain technical concepts to nontechnical audiences.
• Foster experimentation, responsible technology adoption, workforce upskilling, and continuous improvement.

Required Qualifications

• At least 8 years of relevant experience in data science, AI, analytics, automation, product management, technology delivery, audit technology, or related enterprise technology functions.
• At least **5 years o…