AI & Data Governance Lead - Vice President
AI & Data Governance Lead - Vice President
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Job Description
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. Youll 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 theAI & Data Governance LeadforLegal & Regulatory Control Operations (LRCO) you will own the operating model that keeps our AI/ML and data-driven capabilities compliant measurable and scalable. Youll run endtoend governance (intake risk assessment approvals monitoring audit readiness) and coown the LRCOdata strategywith our Data Owners and partners covering data architecture lineage taxonomy quality and consumption patterns. Youll 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 changemanagement 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.
- Coown LRCOs 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 redteaming 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 (usecase 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.
- Bachelors 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.
Metricsdriven: defines KPIs (accuracy/precision/recall timetoresolution costtoserve reliability/latency) and builds telemetry for continuous improvement.
Required Experience:
Chief
Employment Type
Full-Time
