Head of Data Analytics, Automation, AI & Continuous Auditing (DAACA)

The Head of Data Analytics, Automation, AI & Continuous Auditing (DAACA) is a leadership role within Internal Audit, responsible for defining and scaling data-driven capabilities across the audit lifecycle and within audit operations.

This role combines strategy, delivery, and people leadership to embed analytics, automation, and AI into audit planning, execution, and reporting – enhancing audit quality, coverage, efficiency, and insight generation to our stakeholders.

The DAACA function operates as a capability center and strategic enabler, driving innovation while ensuring alignment with Internal Audit standards, data governance, and enterprise risk priorities.

You will

Strategy, Transformation & Value Realization

• Define and execute the DAACA vision, strategy, and multi-year roadmap aligned to Internal Audit and enterprise priorities.

• Position DAACA as a core pillar of audit transformation, advancing continuous assurance and data-driven auditing.

• Identify and prioritize high-value use cases for analytics, automation, and Ai across audits and enterprise risk themes.

• Establish and track KPIs/KRIs (e.g., adoption, coverage expansion, cycle time reduction, insight generation, cost efficiency, etc.).

• Drive value realization and ROI, demonstrating measurable impact to Audit Leadership and the Audit & Risk Committee.

Audit Integration & Continuous Assurance

• Embed analytics and automation into end-to-end audit lifecycle – risk assessment, planning, fieldwork, and reporting.

• Expand continuous assurance, shifting from point-in-time reviews to ongoing risk coverage.

• Standardize reusable analytics and control libraries to improve consistency and scalability.

• Partner with audit teams to increase coverage and reduce manual testing dependency.

Delivery, Engineering & Capability Build

• Oversee development and delivery of scalable, repeatable, and well-governed solutions.

• Implement a product-oriented operating model (e.g., use case backlog, prioritization, iterative delivery, etc.).

• Ensure strong data engineering, data quality, and documentation standards.

• Evaluate and optimize tooling ecosystem.

• Balance transformative initiatives with ongoing delivery (run’ vs ‘build’).

AI Enablement & Governance

• Oversee development and delivery of scalable, repeatable, and well-governed solutions.

• Lead responsible adoption of AI/GenAI within Internal Audit, include use case identification and deployment.

• Establish governance and controls framework(s) for AI usage in audit activities.

• Ensue alignment with enterprise AI, data governance, and model risk frameworks.

• Driver auditor upskilling on AI-enabled auditing techniques.

Stakeholder Engagement & Enterprise Alignment

• Serve as a primary interface between Internal Audit and Technology, Data, AI, and Risk functions.

• Influence adoption by acting as a trusted advisor to audit leadership and business stakeholders.

• Partner with enterprise teams to leverage existing data assets, platforms, and capabilities.

• Support regulatory, QAIP, and stakeholder inquiries related to analytics, automation, and AI.

People Leadership & Capability Development

• Oversee development and delivery of scalable, repeatable, and well-governed solutions.

• Build and lead a high-performing multidisciplinary team (data analysts, data scientists, automation specialists, visualization experts, etc.).

• Define career paths, skill frameworks, and training programs aligned to future IA capabilities.

• Folster a culture of innovation, accountability, collaboration, and continuous improvement.

• Develop a network of “DAACA Champions” embedded across audit teams to drive adoption.

You have

• Bachelor’s degree in a relevant field (e.g., Information Systems, Data Analytics, Engineering, etc.).

• 10+ years of experience in internal/external audit; 5+ years of experience implementing analytics, automation and/or AI within Internal Audit or Risk functions.

• Experience in AI/GenAI use cases and governance considerations.

• Proven ability to translate data/technology capabilities into business and audit value.

• Strong understanding of audit lifecycle, risk management, and control frameworks.

• Demonstrated people leadership and team development experience.

Prefer