VP-DATA/ANALYTICS & AI

The VP of Data, Analytics and AI is an executive leadership role responsible for leveraging data, analytics, and artificial intelligence to drive business and clinical value, foster a data-driven culture, and ensure effective governance and use of Premier's data assets and ecosystem. Reporting to the Chief Digital Information Officer (CDIO), the VP of Data, Analytics and AI leads the execution of Premier's enterprise data, analytics, and AI strategy and operating model, builds executive and Board-level trust in data as a strategic asset; operationalizes enterprise data governance in partnership with security, privacy, risk, and compliance leaders; and develops the talent and culture required to mature Premier's data and analytics capability. This role will partner with senior leaders across Premier Health to understand their data needs and deliver tools to enable them to achieve their business, clinical and operational initiatives.

As a member of the DHT senior leadership team, the VP of Data, Analytics and AI leads day-to-day strategy execution, operating model design, and delivery for the data, analytics, and AI portfolio. This role will work in close coordination with the CDIO's technology infrastructure, Epic platform governance, and cybersecurity functions to ensure a single, unified digital agenda to support the strategic initiatives of Premier Health. The VP of Data, Analytics and AI also plays a leading role in building data governance and analytics infrastructure supporting Premier's Wright State University academic partnership and Academic Medical Center designation, consistent with priorities set by the organization.

Education: Bachelor's Degree in Business Administration, Computer Science, Data Science, Information Systems, Health Informatics, or a related field. Master's degree (e.g., MBA, MS Data Science, MS Health Informatics) preferred.

Licensure/Certification/Registration: None required; Certified Analytics Professional (CAP) or comparable data/AI governance certification preferred.

Experience: Minimum 10 to 15 years of progressive business experience, recently at or near the executive level such as Senior data, analytics, or business intelligence executive (e.g., VP/SVP Data & Analytics, Chief Analytics Officer, Chief AI Officer) with progressive leadership experience required. Must have experience with enterprise data & analytics strategy, data governance, AI enablement, and cross-functional program leadership. Preferred experience: Healthcare or health system experience; academic medical center or university-affiliated data governance experience; strategy or management consulting background.

Other experience requirements: 5 or more years of progressive leadership managing cross-functional, multidisciplinary data and analytics teams across a complex organization.

Knowledge/Skills:

• Enterprise data, analytics, and AI strategy development and execution, including policy development
• Data and analytics governance (master data management, data quality, data stewardship) and connected governance with privacy, security, and compliance functions
• Broad understanding of information architectures (data fabric, data mesh, data warehouse, data lake, data hub) and analytics approaches (descriptive, diagnostic, predictive, prescriptive)
• AI fundamentals, including AI-ready data practices, responsible/ethical AI use, and awareness of applicable healthcare regulations (e.g., HIPAA, information blocking rules, emerging state and federal AI regulation)
• Business and financial acumen; ability to translate data, analytics, and AI concepts into business and clinical outcomes and ROI
• Exceptional executive communication, storytelling, and stakeholder influence across clinical, operational, financial, and Board audiences
• Demonstrated leadership building and managing complex, multidisciplinary teams and driving enterprise-wide culture change
• Experience operating within a CIO/CDIO-led digital governance structure, and partnering effectively with CISO, Compliance, and Legal functions on data trust, security, and governance
• Statistical literacy (e.g., understanding the difference between correlation and causation) sufficient to guide analytically rigorous decision-making