Model Governance Manager (Data Science)

As a Model Governance Manager, you will play a key role in operating and enhancing AI governance processes within Data Science. You will partner with data scientists, model owners, and governance stakeholders to support model approvals, inventory management, regulatory compliance, and the continued evolution of a scalable governance framework that enables responsible AI innovation.

Responsibilities

• Own and run day-to-day AI/model approval and review processes, ensuring consistent and timely turnaround.
• Maintain the third-party software and model inventory model inventory as the authoritative record of models in development and production.
• Act as the primary day-to-day point of contact for AI software requesters, data scientists and model owners navigating governance requirements.
• Track and report on the health of the model portfolio, 3rd party AI software and the effectiveness of approval processes.
• Manage and continuously improve governance products supporting approvals and inventory management.
• Identify opportunities for automation, self-service capabilities, and process improvement.
• Support the evolution of governance policies, standards, and risk assessment methodologies.
• Maintain audit-ready documentation and support compliance, audit, and risk management activities.

Requirements

• A data science background with hands-on experience building, training, validating, or deploying machine learning models, gained as a data scientist, ML engineer, or in a closely related technical role.
• Experience with, or strong interest in, AI/model governance, model risk management, or a related risk/compliance discipline.
• Strong organizational skills and comfort owning operational processes end-to-end, including maintaining structured records such as a model inventory.
• Practical understanding of relevant AI/data regulation and standards.
• A continuous-improvement mindset.
• Strong communication skills, able to explain governance requirements clearly to technical model owners and translate technical detail for non-technical stakeholders.
• Ability to work independently on operational delivery while contributing constructively to longer-term framework design.
• Prior experience with model inventory, MLOps, or model risk management tooling, and exposure to governance framework or policy development.