Manager, Internal Audit - AI Strategy & Innovation

The role:

We're looking for a visionary and technical Manager to architect and lead the integration of Artificial Intelligence (AI) into our Internal Audit function. This strategic role is responsible for driving innovation across the entire audit lifecycle, from risk assessment to continuous monitoring. The Manager will define the AI strategy and framework, oversee the technical deployment of AI solutions, and serve as the department's foremost expert on auditing the risks of the organization’s own AI systems and ensuring responsible AI governance.

What you’ll do: 

AI Strategy, Methodology & Governance

• Define AI Strategy & Methodology: Develop, socialize, and govern a forward-looking AI methodology and framework to guide the Internal Audit department's adoption and responsible use of AI.
• Identify Opportunities Across the Audit Lifecycle: Proactively identify and prioritize high-impact AI opportunities across the entire audit lifecycle (e.g., risk assessment, planning, testing, reporting, follow-up, and continuous auditing).
• Feasibility & Working Group Alignment: Determine the technical and business feasibility of AI use cases and present them to the organization's AI Working Group for approval and resource allocation.
• Responsible AI Governance (SME Role): Act as the department's Subject Matter Expert (SME) on auditing the organization's enterprise-wide AI systems, ensuring adherence to regulatory, ethical, and responsible AI governance standards.
• Develop & Measure Pilots: Develop AI pilot programs across various stages of the audit lifecycle, and rigorously measure their effectiveness and efficiency gains to justify scaling.
• Partner with other relevant teams within the organization to understand practical AI applications and determine potential synergies.

Deployment, Partnership & Execution

• Continuous Monitoring/Auditing & Automation: Partner with the Data Analytics team to strategically determine and implement use cases for audit fieldwork execution, continuous monitoring, continuous auditing, and automation, moving the department toward real-time assurance.
• Professional Practices Group Collaboration (PPG): partner with PPG to optimize technology enablement opportunities using core tools (e.g. AuditBoard).
• IT Audit Collaboration: Partner closely with Internal Audit's IT Audit team to integrate AI assurance procedures into the IT audit plan and evaluate the integrity of the underlying AI infrastructure and controls.
• Vendor Management: Partner with and manage third-party vendor relationships related to AI tooling, ensuring solutions meet technical requirements, security protocols, and strategic objectives.
• End-to-End Deployment: Oversee the full deployment lifecycle of AI tools, ensuring successful technical implementation, data integration, security, and sustained maintenance.

Team Enablement & Training

• Develop and Deliver Training: Develop and deliver comprehensive training materials and programs to upskill all audit personnel on AI concepts, data literacy, and the practical application of new AI tools.
• Technical Coaching: Provide dedicated, hands-on coaching and technical mentorship to drive the practical adoption of new technologies within audit teams.

What you’ll need:

• Deep AI & Technical Acumen: Must have a strong understanding of current AI tools, machine learning concepts (e.g., supervised, unsupervised, deep learning), and the technical requirements for deploying models in a production environment.
• Data Analytics Proficiency: Strong command of data analytics tools and techniques (Python, R, SQL, cloud environments) for data extraction, transformation, analysis, and visualization.
• Audit Risk Frameworks: Exceptional knowledge of Internal Audit standards (IIA), risk management (COSO), and control design, specifically as they relate to automated controls and algorithmic decision-making.
• Communication & Leadership: Proven ability to translate highly technical AI concepts and risks into clear, actionable, and value-driven insights for executive leadership and non-technical stakeholders.
• Project Management: Demonstrated experience leading complex, cross-functional technology projects, managing timelines, budgets, and stakeholder expectations.
• Education: Bachelor’s degree in Computer Science, Data Science, Information Technology, Accounting, Finance, or a related quantitative field. A Master’s degree is a strong plus.
• Experience:
• Minimum 7+ years of progressive experience in Internal Audit, IT Audit, Risk Consulting, or a specialized data/AI role within a regulated industry.
• Minimum 3+ years of direct experience in developing, deploying, and managing data analytics, AI, or automation projects.
• Must have demonstrated success in deploying AI tools to solve complex business problems.
• Certifications: Professional certification is required (e.g., CIA, CISA, CPA). Specialized AI/Data Science certification is highly desirable.