Senior Manager - Data & AI Governance

Mercury is seeking a Data & AI Governance leader to build and spearhead an enterprise-wide governance program for data and artificial intelligence. Reporting to the Chief Risk Officer, this leader will establish practical standards for how data and AI are owned, developed, used, protected, and monitored across the organization. The ideal candidate combines strong governance and risk-management experience with sufficient technical fluency to work effectively with Data, Engineering, Product, Information Security, Legal, Compliance, and business teams. This person should be comfortable building in a fast-moving environment and designing governance that supports responsible innovation without creating unnecessary complexity. This role will work closely with the Model Risk Management and Information Security teams while maintaining a distinct mandate: Data & AI Governance will establish enterprise governance and responsible-use standards, while Model Risk Management will retain responsibility for model inventory, tiering, validation, and model-risk oversight. Key Responsibilities: Develop and implement Mercury’s enterprise Data and AI Governance frameworks, policies, standards, and operating model. Establish clear accountability for data ownership, stewardship, quality, lineage, classification, access, retention, and appropriate use. Create a risk-based governance process for AI use cases across their lifecycle, including intake, assessment, approval, implementation, monitoring, and retirement. Develop responsible-AI principles and standards addressing transparency, explainability, fairness, privacy, security, human oversight, reliability, and regulatory compliance. Maintain an enterprise inventory of material data assets, AI use cases, and related governance decisions in coordination with relevant stakeholders. Define risk-based classifications and governance requirements based on the sensitivity, complexity, materiality, and customer or regulatory impact of each use case. Establish governance for internally developed, vendor-provided, and embedded AI capabilities, including generative AI. Partner with various Product, Engineering, Data, and business teams to embed governance requirements into development and change-management processes. Coordinate with Model Risk Management to determine when an AI use case meets the definition of a model and is subject to model-risk requirements. Partner with Information Security and Technology Risk on data protection,