Data and AI Governance Product Lead- Vice President

JPMorganChase•Jersey City, NJ

About The Position

The Data and AI Governance Product Lead, Vice President, in the Chief Data Office, Product Management, owns the end-to-end lifecycle of data and AI governance products, from discovery through delivery and adoption. The role translates business needs into scalable product capabilities that reduce risk and increase business value, and drives prioritization through a clear roadmap and a well-managed backlog. The position focuses on building and scaling governance products that improve decision-making, strengthen controls, and improve data outcomes across a global financial institution. It shapes a modern product roadmap centered on automation, standardization, and measurable risk reduction, while collaborating with product, engineering, data science, legal, and risk partners. Solutions should be easy to adopt and designed for auditability, with governance-by-design controls, documentation, and compliance protocols embedded throughout delivery.

Requirements

Seven or more years of relevant experience in product management, data strategy, program management, consulting, or internal strategy within financial services, fintech, or a similarly regulated industry.
Demonstrated ability to independently identify opportunities, set direction in ambiguous environments, and deliver measurable outcomes.
Advanced understanding of the product development lifecycle, from discovery through delivery, including product design, agile delivery practices, analytics, and operating model considerations.
Proven experience leading end-to-end product lifecycle activities, including discovery, ideation, roadmap development, requirements definition, prioritization, and value measurement.
Bachelor’s degree or equivalent practical experience in a relevant discipline such as data, computer science, business analytics, or engineering.
Working knowledge of data and AI governance concepts and ability to translate technical topics into practical product solutions.
Strong analytical and structured problem-solving skills, including comfort using quantitative methods and data-driven decision-making.
Strong attention to detail and ability to synthesize complex information accurately and efficiently.
Excellent written and verbal communication skills, with experience influencing senior stakeholders and cross-functional partners.
High ownership and accountability, with a track record of proactively managing risks and following through to completion.

Nice To Haves

Advanced degree in a quantitative or technical discipline.
Experience building or enhancing data governance products, workflows, or controls in an enterprise environment.
Experience delivering automation and standardization initiatives that improve data outcomes and reduce operational risk.
Familiarity with governance patterns for analytics and artificial intelligence and machine learning use cases, such as model controls, documentation, and monitoring expectations.
Demonstrated experience leading change management and driving user adoption for new tools or processes.

Responsibilities

Lead end-to-end design and delivery of data and AI governance products that translate business needs into scalable capabilities.
Own and continuously refine a prioritized product backlog aligned to the strategic roadmap.
Prioritize high-impact features with an emphasis on automation, standardization, and innovation.
Partner with stakeholders across product, engineering, data science, legal, and risk to modernize governance processes and operating models.
Drive execution from concept to delivery, including problem definition, requirements, planning, stakeholder alignment, and user adoption.
Represent stakeholder needs by making transparent trade-offs across control obligations, technical feasibility, and business outcomes.
Manage multiple concurrent initiatives by setting milestones, dependencies, and delivery cadence.
Embed governance-by-design controls, documentation, auditability, and compliance protocols throughout delivery.
Communicate complex technical and risk topics clearly to non-technical stakeholders and senior leaders.
Monitor delivery health by reporting status, escalating issues, and recommending mitigation actions.