Back to jobsAI Governance Framework Implementation: Establishes, implements, and advances McCormick’s enterprise AI governance framework, including the design and application of policies, standards, and processes that guide responsible AI development and use across global functions. Defines governance processes across the AI lifecycle (design, development, deployment, monitoring, and retirement) to ensure transparency, fairness, and accountability.
AI Risk Management and Assessment: Conducts AI risk assessments for new or evolving AI use cases to evaluate ethical, regulatory, and operational risks. Defines and monitors key risk indicators for AI systems (e.g., bias, model drift, explainability, hallucination). Oversees AI model inventory, classification, and tiering based on risk and business impact, ensuring alignment with McCormick’s enterprise risk management and compliance frameworks.
AI Governance Technology Enablement: Partners with Technology and Data & Analytics teams to implement and optimize AI governance tools and platforms (e.g., model registry, documentation and monitoring tools, compliance dashboards). Ensures governance capabilities—such as model lineage tracking, transparency reporting, and automated policy enforcement—are effectively integrated into McCormick’s broader data and analytics ecosystem. Supports continuous improvement and scalability of these tools to enable trusted and efficient AI oversight.
Responsible AI Governance Operations: Leads the cross‑functional Responsible AI Forum, coordinating participation from Data & Analytics, Technology, Legal, Privacy, and business functions to ensure consistent application of responsible AI principles. Facilitates governance reviews, documentation, and decision‑making processes related to AI use cases. Supports the development of AI transparency reports, audit artifacts, and governance dashboards that demonstrate compliance and ethical standards.
Continuous Improvement and Performance Measurement: Defines and tracks metrics to assess AI governance maturity, compliance, and effectiveness. Identifies opportunities for automation, process optimization, and integration between AI and data governance activities. Continuously improves governance capabilities to support scalable, transparent, and trusted AI innovation that accelerates business value and ensures responsible use of AI across the enterprise.