AI Program Manager - Data Impact & Governance

Summary:

The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, prevention, and education. Core to the success of our mission is the ability to orchestrate multidimensional data, data analytics, and machine learning to drive decisions that are safer, faster, and proven to improve outcomes. Join us as we turn data into a lasting impact for every patient we serve.

We are seeking an AI Program Manager to support the operationalization of our closed loop AI strategy. This role is responsible for translating vision into coordinated execution that enables safe, scalable, and outcome-driven AI across the organization. This role aligns AI governance, AI lifecycle management, platform capabilities, and education to embed AI into clinical and operational practice. You will oversee the closed-loop delivery process: from governance-informed risk control plans and lifecycle monitoring to platform enablement and workforce adoption. Success in this role means that AI initiatives are not only deployed with precision and quality, but are trusted, adopted, and measurably improving patient and operational outcomes.

Core Responsibilities include:

Maintain closed-loop coordination across AI Governance, Operations, Platforms, and external teams to ensure AI initiatives progress consistently and with quality.

Track and align activities to ensure AI initiatives meet compliance, regulatory, and ethical requirements.

Ensure risk assessments, AI Assurance evaluations, and documentation (e.g., model cards, impact analyses) are completed for all AI initiatives.

Collect and report objective outcome metrics demonstrating safety, effectiveness, and operational impact of deployed AI.

Track post-deployment model monitoring, including drift detection, retraining needs, and adherence to safety thresholds.

Coordinate platform readiness for deployment, monitoring, and decommissioning activities in alignment with governance requirements.

Support structured stakeholder engagement to capture feedback, address adoption challenges, and maintain alignment across teams.

Maintain transparent logs of risks, issues, and actions, escalating to governance and leadership when needed.

Facilitate feedback processes to continuously improve AI initiatives while ensuring measurable, safe, and compliant delivery.

Support education and training initiatives to enhance understanding and application across the organization.

Engage with technology trends, contribute to tech communities, and foster a culture of continuous learning and innovation.

Track and report the operational costs and value generated for each AI initiative to support data-driven prioritization and resource decisions.

Technical Expertise    
Understanding of the AI/ML lifecycle, including data readiness, model validation, deployment, monitoring, and retraining.

Familiarity with AI governance and compliance frameworks (e.g., ISO42001, FDA AI/ML guidance, HIPAA, model documentation standards).

Ability to interpret and track model performance and operational metrics (e.g., AUC, drift, latency, adoption rates).

Data literacy, including the ability to analyze structured dashboards and interpret outcome metrics.

Awareness of technical platforms used in AI deployment (e.g., model registries, monitoring tools, integration with clinical systems).

Proficiency with project and resource tracking tools (e.g., MSFT Projects, Jira, or similar) for managing programs, projects, resource allocations.

Basic understanding of data privacy and security principles relevant to AI initiatives.

Ability to communicate technical issues clearly with data scientists, engineers, and stakeholders.

Analytical Expertise  
Skilled in applying project management methodologies (e.g., SAFe Agile, Lean) to track timelines, and quality across the AI/ML project lifecycle.

Ability to quantitatively assess machine learning models for performance, workflow impact, and potential risks.

Proficient in evaluating total cost, expenditures, and ROI for AI initiatives to support data-driven prioritization and resource decisions.

Capable of interpreting outcome metrics and operational data to generate clear, actionable insights for program improvement.

Competent in identifying risks and developing mitigation plans to maintain compliance, safety, and timely delivery.

Adept at collaborating with vendors and partners to evaluate and integrate third-party AI solutions into existing systems.

Oral and Written Communication   
Collaborate effectively with AI governance, AI/ML operations and platform teams, and other operational stakeholders to ensure seamless delivery of AI initiatives.

Report concise project metrics on progress, impact, and risks, providing clear recommendations to leadership on the AI program.

Manage stakeholder relationships to support program operations and address issues promptly.

Deliver clear technical and non-technical program updates in meetings and professional forums. 

Other duties as assigned