Senior Director Internal Audit - Data Science And AI

Senior Director – Internal Audit - Data Science and AILocation: Reston, VA 20190 / Washington, DC / Remote within the United StatesWork Arrangement: Hybrid for candidates local to Washington, DC or Reston, VA; remote consideration for eligible candidates residing elsewhere in the 50 U.S. statesEmployment Type: Full-TimeWork Hours: Eastern Time business hours, 8:30 AM – 5:00 PM ETThe 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 ResponsibilitiesAI 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 of leadership experience building, coaching, and leading high-performing, diverse technical teams.
• Demonstrated success delivering enterprise AI, analytics, automation, or data products from strategy through production adoption, with measurable business outcomes.
• Experience leading AI-enabled transformation in Internal Audit, risk, compliance, financial services, or another highly regulated enterprise environment.
• Deep expertise in AI/ML, GenAI, automation, advanced analytics, data platforms, cloud technologies, model lifecycle management, and scalable software/product delivery.
• Experience partnering with Technology to build enterprise platforms, reusable capabilities, microservices, governed data products, APIs, and AI solutions.
• Strong understanding of data governance, model risk, responsible AI, and regulatory expectations in regulated environments.
• Executive-level communication, stakeholder influence, and strategic advisory skills.
• Experience developing strategies, operating models, talent plans, and performance measures that improve delivery, reuse, workforce adoption, and efficiency.
• Bachelor's degree or equivalent experience in Computer Science, Data Science, Statistics, Engineering, Information Systems, Business, or a related field.
• Curiosity and adaptability in learning and responsibly applying emerging technologies, including AI.

Preferred Qualifications

• Advanced degree in Computer Science, Data Science, Statistics, Engineering, AI, Analytics, Business, Finance, Audit, Risk, or a related discipline.
• Strong understanding of Internal Audit, controls, or enterprise risk management.
• Experience with GenAI/LLMs, retrieval-augmented generation (RAG), intelligent agents, NLP, anomaly detection, machine learning, multi-agent workflows, or knowledge graphs.
• Familiarity with cloud AI and data platforms, MLOps/LLMOps, model monitoring, data quality controls, secure software delivery, and enterprise architecture.
• Relevant AI, machine learning, cloud, or technology certifications, or equivalent professional experience.