Manager 

Job Description

Responsibilities:

• Develop and implement 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, cybersecurity, access, architecture, resilience, and technology-control considerations.
• Partner with Legal and Compliance to identify and implement applicable regulatory, contractual, consumer-protection, and privacy requirements.
• Develop processes for identifying, documenting, escalating, and remediating data- and AI-related risks and issues.
• Establish metrics/reporting to provide management and Board committees with visibility into data quality, governance maturity, AI adoption, exceptions, incidents, and emerging risks.
• Monitor regulatory developments, industry practices, and emerging risks related to data and AI, and translate them into proportionate governance expectations.
• Support relevant Data and AI governance forums/committees and facilitate timely, well-documented decisions.
• Eventually, build and lead a high-performing Data & AI Governance team as the program matures.
• Promote a culture in which data is treated as an enterprise asset and AI is used responsibly, transparently, and in alignment with risk appetite.

Qualifications:

• 10+ years of relevant experience in data governance, AI governance, technology risk, information governance, model risk, privacy, compliance, or a related discipline.
• Demonstrated experience building or materially enhancing a data governance, AI governance, or responsible-AI program.
• Strong understanding of data ownership, stewardship, quality, lineage, metadata, classification, access, retention, and lifecycle management.
• 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.
• Experience developing practical, risk-based policies and governance processes that can operate effectively in a fast-moving technology environment.
• Ability to distinguish among data governance, AI governance, model risk, information security, privacy, and compliance responsibilities while coordinating effectively across those functions.
• Strong judgment and the ability to balance innovation, customer outcomes, regulatory expectations, and risk management.
• Demonstrated ability to influence senior executives, technical teams, and business leaders without relying solely on formal authority.
• Excellent written and verbal communication skills, including the ability to explain complex technical and risk concepts to executive and Board audiences.
• Experience leading teams and managing cross-functional programs with multiple stakeholders.
• Strong 1LOD/2LOD judgment with an understanding of how enterprise Risk should govern, challenge, and partner with Engineering without taking ownership of 1LOD risks.
• Pragmatic judgment: Translates principles into workable processes and focuses on material risks over theoretical ones.
• Technical curiosity: Understands technical complexity while staying focused on business and customer outcomes.
• Decisive and collaborative: Makes sound decisions amid ambiguity, moves quickly, and challenges constructively across teams

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