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

Guardian Life Insurance•New York, NY

5d•Hybrid

About The Position

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.

Requirements

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.
Must be legally authorized to work in the United States, without the need for employer sponsorship.

Nice To Haves

Experience with Python, SQL, Power BI/Tableau, Power Automate, Power Query, Alteryx, or similar tools.
Experience with Copilot Studio and Claude Cowork.
Experience in agile or product-based delivery models.
Professional certifications (e.g., CIA, CISA, CPA, etc.).

Responsibilities

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.
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.
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’).
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.
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.
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.