Chief Data & AI Officer (CDAO)

Job Summary

Michigan Medicine is seeking a Chief Data & Artificial Intelligence Officer (CDAO) to provide executive leadership for the enterprise data, analytics, and AI ecosystem, enabling a data-driven, AI-enabled academic health system.

Reporting to the Chief Digital & Information Officer (CDIO), the CDAO is accountable for the strategy, governance, architecture, and enablement of data and AI capabilities across Michigan Medicine. This role ensures data is trusted, accessible, governed, and translated into actionable intelligence, while enabling scalable, responsible adoption of artificial intelligence.

The CDAO plays a central role in supporting Michigan Medicine's evolution into a learning health system, orchestrating data and AI capabilities across clinical, operational, academic, and research domains. The role emphasizes platform enablement over centralization, fostering a distributed analytics and democratized AI model supported by strong governance, shared standards, and modern tooling.

Organizational Scope

The CDAO provides enterprise leadership across core domains including:

• Enterprise Data Strategy & Governance

• Data Platforms, Architecture & Engineering

• Clinical & Operational Analytics (including Epic Cogito)

• Artificial Intelligence & Advanced Analytics (AI/ML/GenAI)

• Machine Learning Operations (MLOps) & AI Lifecycle Management

• Data Literacy, Self-Service Analytics & AI Enablement

• Data Infrastructure & Data Use Enablement

• AI Governance, Ethics & Responsible AI

• AI Orchestration, Automation, and Agentic Monitoring

Responsibilities*

Key Responsibilities

Enterprise Data & AI Strategy

• Define and execute a comprehensive enterprise data and AI strategy aligned with Michigan Medicine's clinical, operational, academic, and research priorities.

• Position Michigan Medicine as a leader in AI-enabled healthcare delivery, academia, research, and operations.

• Serve as the executive advisor on data, analytics, and AI investments, opportunities, and risks.

Data Governance, Strategy & Stewardship (Enterprise Ownership)
 

• Partnering across the enterprise, establish and lead enterprise data governance, including:

  • Data ownership and stewardship models

  • Data policies, standards, and controls

  • Data quality, integrity, and trust frameworks

• Define and enforce data management standards, including:

  • Metadata, cataloging, and lineage

  • Data classification and access controls

  • Master and reference data strategies

• Ensure all data assets are secure, compliant, governed, and usable at scale.

Data Platforms, Architecture & Infrastructure

• Lead enterprise data platform strategy and delivery, including:

  • Data architecture and engineering

  • Data pipelines, integration, and interoperability

  • Scalable data environments supporting clinical, administrative, academic, and research workloads

• Oversee clinical data infrastructure and architecture, enabling:

  • Longitudinal patient records

  • Master Data Management

  • Interoperability across systems and partners

  • Support for advanced analytics and AI

• Lead and optimize the Epic Cogito environment, ensuring it is:

  • Integrated into the broader enterprise data ecosystem

  • Performing, scalable, and aligned with reporting and analytics needs

• Partner with the CTO to ensure alignment between:

  • Data platforms

  • Underlying infrastructure and cloud environments

  • Integration and interoperability platforms (API management, middleware)

Analytics & Data Enablement (Distributed Model)
 

• Enable a distributed analytics model by:

  • Providing shared platforms, tools, and governed access

  • Supporting domain-based analytics across clinical, operational, academic, and research teams

• Lead enterprise analytics capabilities, including:

  • Clinical, operational, and financial analytics

  • Revenue cycle, administrative, and performance analytics

  • Research and academic analytics

  • Self-service BI tools and reporting environments

• Promote data democratization, ensuring users can:

  • Access trusted data

  • Build insights independently

  • Operate within governance guardrails

Artificial Intelligence & Advanced Analytics
 

• Lead enterprise AI strategy, including:

  • Predictive analytics

  • Machine learning and deep learning

  • Generative AI and agent-based systems

• Identify, prioritize, and scale high-impact AI use cases across clinical, operational, and research domains.

• Partner with CHIO to enable clinical decision support and AI in care delivery.

• Partner with CAO to embed AI into applications and workflows.

MLOps, AI Orchestration & Agentic Monitoring
 

• Establish and lead Machine Learning Operations (MLOps) capabilities, including:

  • Model development pipelines

  • Deployment, monitoring, and lifecycle management

  • Model versioning, retraining, and performance tracking

• Implement enterprise capabilities for:

  • AI orchestration across systems and workflows

  • Agentic AI management and monitoring

  • Continuous validation of model performance, drift, and bias

• Ensure AI is:

  • Scalable and production-ready

  • Continuously monitored and improved

  • Integrated into enterprise workflows

AI Governance, Ethics & Responsible Use
 

• Establish enterprise frameworks for AI governance and ethical use, including:

  • Model transparency and explainability

  • Bias detection and mitigation

  • Accountability and oversight

• Partner with legal, compliance, and clinical leadership to ensure:

  • Responsible AI deployment

  • Regulatory alignment

  • Patient safety and trust

Data Literacy, AI Enablement & Innovation
 

• Promote a culture of data literacy and AI fluency across the organization.

• Establish enterprise capabilities for:

  • Training, enablement, and adoption

  • Self-service analytics and AI tools

  • Innovation sandboxes and experimentation environments

• Enable "vibe coding" and democratized AI experimentation in a controlled, governed environment.

Cross-Functional Leadership & Integration
 

• Partner with: 

  • Application leaders - embed data and AI into applications

  • Technology leaders - align data and infrastructure platforms

  • Customer Experience - drive adoption of analytics and AI tools

  • Health Informatics -  align AI and analytics to clinical workflows

  • Academics and Research - enable research data, advanced analytics, and AI

  • Enterprise Delivery -  integrate data/AI into portfolio prioritization and execution

Financial, Vendor & Workforce Leadership
 

• Lead investment planning and financial management for data and AI capabilities.

• Manage relationships with vendors and platform providers in data, analytics, and AI.

• Build and lead high-performing teams across: 

  • Data engineering and architecture

  • Analytics and BI

  • AI/ML engineering

  • Governance and enablement

     

Required Qualifications*

• Bachelor's degree in Data Science, Computer Science, Informatics, Engineering, or related field.

• 15+ years of experience in data, analytics, and/or AI leadership roles.

  • 10+ years of leadership experience managing large, multidisciplinary teams.

• Demonstrated experience building and scaling enterprise data platforms and AI capabilities.

• Deep expertise in: 

  • Data architecture and engineering

  • Analytics and BI

  • AI/ML and modern data ecosystems

• Proven ability to operate in complex, federated, healthcare or academic environments.

Desired Qualifications*

• Master's or PhD in Data Science, Informatics, Computer Science, Engineering, or related discipline.

• Experience in an academic medical center, health system, or research environment.

• Experience with Epic Cogito and healthcare data ecosystems.

• Track record of deploying AI at scale in research, academic, operational or clinical settings.

Modes of Work

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

Additional Information

Benefits Information

We offer a benefits package that includes comprehensive training and career development opportunities, generous retirement savings plans, ample paid time off, and a wealth of family care support: https://careers.umich.edu/benefits/ 

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

U-M EEO Statement

The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.