AI & Data Governance Lead - Vice President

Be the accountable leader for AI and data governance in LRCO operationalizing Responsible AI, risk, and data management controls while partnering with Data Owners to shape and execute the LRCO data strategy.

You are passionate about turning governance into an accelerator not a blocker. You'll lead the frameworks, processes, and data foundations that let teams ship compliant, explainable AI and analytics at scale. Join us to set the standard for safe innovation in Legal & Regulatory Control Operations, from intake to production, across ML, NLP, retrieval, GenAI and automation.

Job summary

As the AI & Data Governance Lead for Legal & Regulatory Control Operations (LRCO), you will own the operating model that keeps our AI/ML and data-driven capabilities compliant, measurable, and scalable. You'll run end‑to‑end governance (intake, risk assessment, approvals, monitoring, audit readiness) and co‑own the LRCO data strategy with our Data Owners and partners covering data architecture, lineage, taxonomy, quality, and consumption patterns. You'll partner across Product, Engineering/ML, Legal, DUC, MRGR, and Data Owners to embed controls (privacy, explainability, fairness, security, HITL) and to align with firmwide standards so we can deliver measurable customer value without compromising safety or compliance.

Job responsibilities

• Leverage strong communication skills to lead project teams through challenges and work with key stakeholders (higher, peer, lower in the organizational structure) via influencing and negotiating.  
• Own the AI governance strategy for LRCO and embed it into the product roadmap aligning to firmwide and CCB procedures (registration, approvals, monitoring, documentation).  
• Run the AI/ML governance tollgates (registration, governance forums, etc.) and ensure change‑management reviews where applicable.  
• Present on model risk governance: For determination of Model vs Analytical Tool designation, track submissions/waivers, secure approvals, and set ongoing monitoring and documentation standards.  
• Operationalize Responsible AI controls with Legal/CCOR (HITL, fairness/bias, explainability, privacy, security) and maintain compliance with prohibited AI checks and EU AI Act classifications.  
• Co‑own LRCO's data strategy with Data Owners covering architecture, taxonomies, metadata/lineage, store registration, gold source consumption and retention, and quality SLAs.  
• Establish robust evaluation & monitoring: offline/online testing, quality metrics (precision/recall), bias/fairness reviews, red‑teaming, security testing, drift/latency monitoring, and incident playbooks.  
• Define standards for GenAI platform usage: prompt and knowledge base governance, vector store and content filter policies, audit logging, and HITL acceptance criteria. Reporting & transparency: publish governance dashboards (use‑case pipeline, model inventory, risk posture, benefits realization) and brief leadership and auditors.  
• Coach and enablement: deliver training and playbooks to uplift LRCO teams on governance, privacy, data quality, and evaluation practices.

Required qualifications, capabilities, and skills

• Minimum 8 years in data governance, risk, compliance, or product; 3+ years leading AI/ML or GenAI governance in regulated environments.
• Bachelor's degree required; advanced degree in data/AI, risk, or related field.
• Deep knowledge of AI/ML governance and model risk processes (model vs. analytical tool designation, submissions, controls, monitoring).
• Proficient knowledge of the product development life cycle, Experience in product life cycle activities, including discovery and requirements definition.
• In-depth knowledge of Agile process and principles, including use of Agile project management tools (i.e., Confluence, JIRA, Git, etc.)
• Strong grasp of Responsible AI practices (HITL, privacy, fairness, explainability) and awareness of EU AI Act categories and prohibited AI checks. Proven experience partnering with Legal and risk teams to translate policy into product and control requirements.
• Expertise in data management (lineage, metadata, taxonomy, store registration, data quality) and working with Data Owners.
• Fluency with data risk and privacy standards (data classification, retention, personal information policies).
• Strong stakeholder management across Product, Engineering/ML, CDAO, and business leaders; excellent written/spoken communication.

• Metrics‑driven: defines KPIs (accuracy/precision/recall, time‑to‑resolution, cost‑to‑serve, reliability/latency) and builds telemetry for continuous improvement.