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Data and AI Governance Lead, Computing Services
Role at a glance
- Category
- AI Governance
- Work arrangement
- On-site
- Location
- Pittsburgh, PA
- Posted
- Jul 15, 2026
Carnegie Mellon University is hiring a Data and AI Governance Lead, Computing Services in Pittsburgh, PA. This is a AI Governance role in the governance, risk, and compliance field. Review the full details below and apply directly with Carnegie Mellon University.
Carnegie Mellon University (CMU) is seeking a dynamic, highly collaborative Data and AI Governance Lead to shape and operationalize the framework for how our institution manages, protects, and leverages its most critical data and AI assets. Unlike traditional management roles, this position does not oversee a direct team. Instead, you will act as the university’s primary architect and facilitator of Data and AI Governance, working across academic and administrative units to drive progress through influence, partnership, and expert support. You must be comfortable toggling between high-level strategy (policy-making and risk frameworks) and hands-on delivery (implementing metadata tools and assisting data stewards) Key Responsibilities: Strategy & Framework Design : Lead the design and execution of CMU’s Data and AI governance operating model. Define the policies, standards, and roadmaps that ensure data integrity, privacy, and the responsible use of AI across the university. Influence & Community Building : Build and enable a robust community of practice among distributed Data Stewards and AI practitioners. You will not have "command and control"; authority; success will depend on your ability to build trust, provide value to partners, and align departmental goals with university-wide standards. AI Governance & Risk: Design and operationalize AI governance controls, including model inventories, risk assessments, and ethical reviews. Partner with academic and administrative leads to ensure AI deployment is transparent, fair, and compliant with emerging regulations. AI Data Readiness: Transform our Data assets by establishing a unified metadata layer. You will act as the translator between business Data Analysts, SMEs, and our Data/Analytics Engineering teams, ensuring that business terminology is mapped accurately (e.g., ensuring an AI agent understands that a "Full-Time Student" in a financial aid context (12+ credits) is distinct from "Full-Time Equivalent (FTE)" used for state reporting or faculty workload so engineers can build accurate semantic models for varying audiences. Hands-on Operational Support : "Roll up your sleeves"; to help departments implement governance tooling. Assist in defining data lineage, improving data quality, and documenting metadata for complex institutional datasets. Cross-Functional Collaboration: Serve as the primary bridge between Information Technology, Information Security Office, the Office of the Provost, and
Full responsibilities and requirements are on Carnegie Mellon University's application page.
Apply for this role →Location and market context
This role is based in Pittsburgh, PA on-site. Local candidates benefit from being close to Carnegie Mellon University's teams and regional hiring market. Confirm the exact in-office expectation and any relocation support with the employer.
About AI governance roles
AI governance sits at the intersection of policy, risk, and engineering. Teams are standing up model inventories, use-case intake and review, risk classification, and control monitoring as regulation and board scrutiny of AI intensify. Roles like this one are typically evaluated against frameworks such as NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices.
How to position yourself for this AI governance role
Strong candidates emphasize experience translating policy into operational controls, working across legal, compliance, security, product, and data teams, documenting AI system risks, and supporting governance processes. In your resume and outreach, tie your experience to how Carnegie Mellon University would apply NIST AI RMF, ISO/IEC 42001, the EU AI Act, and internal model-risk and privacy practices, and lead with concrete outcomes rather than duties.
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