Senior Manager - Data & AI Governance

<p><span style="font-size: 14px;">Mercury is seeking a Data &amp; 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.</span></p> <p>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.</p> <p>This role will work closely with the Model Risk Management and Information Security teams while maintaining a distinct mandate: Data &amp; 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.</p> <h3>Key Responsibilities:</h3> <ul> <li>Develop and implement Mercury’s enterprise Data and AI Governance frameworks, policies, standards, and operating model.</li> <li>Establish clear accountability for data ownership, stewardship, quality, lineage, classification, access, retention, and appropriate use.</li> <li>Create a risk-based governance process for AI use cases across their lifecycle, including intake, assessment, approval, implementation, monitoring, and retirement.</li> <li>Develop responsible-AI principles and standards addressing transparency, explainability, fairness, privacy, security, human oversight, reliability, and regulatory compliance.</li> <li>Maintain an enterprise inventory of material data assets, AI use cases, and related governance decisions in coordination with relevant stakeholders.</li> <li>Define risk-based classifications and governance requirements based on the sensitivity, complexity, materiality, and customer or regulatory impact of each use case.</li> <li>Establish governance for internally developed, vendor-provided, and embedded AI capabilities, including generative AI.</li> <li>Partner with various Product, Engineering, Data, and business teams to embed governance requirements into development and change-management processes.</li> <li>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.</li> <li>Partner with Information Security and Technology Risk on data protection, cybersecurity, access, architecture, resilience, and technology-control considerations.</li> <li>Partner with Legal and Compliance to identify and implement applicable regulatory, contractual, consumer-protection, and privacy requirements.</li> <li>Develop processes for identifying, documenting, escalating, and remediating data- and AI-related risks and issues.</li> <li>Establish metrics/reporting to provide management and Board committees with visibility into data quality, governance maturity, AI adoption, exceptions, incidents, and emerging risks.</li> <li>Monitor regulatory developments, industry practices, and emerging risks related to data and AI, and translate them into proportionate governance expectations.</li> <li>Support relevant Data and AI governance forums/committees and facilitate timely, well-documented decisions.</li> <li>Eventually, build and lead a high-performing Data &amp; AI Governance team as the program matures.</li> <li>Promote a culture in which data is treated as an enterprise asset and AI is used responsibly, transparently, and in alignment with Mercury’s risk appetite.</li> </ul> <h3>Qualifications:</h3> <ul> <li>10+ years of relevant experience in data governance, AI governance, technology risk, information governance, model risk, privacy, compliance, or a related discipline.</li> <li>Demonstrated experience building or materially enhancing a data governance, AI governance, or responsible-AI program.</li> <li>Strong understanding of data ownership, stewardship, quality, lineage, metadata, classification, access, retention, and lifecycle management.</li> <li>Working knowledge of AI and machine-learning concepts, including generative AI, large language models, training and inference data, explainability, bias, performance monitoring, and human oversight.</li> <li>Experience developing practical, risk-based policies and governance processes that can operate effectively in a fast-moving technology environment.&l