AI Product Manager - Governance (senior Associate)

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Corporate Sector

AI Product Manager focused on governance, policies, and compliance for AI/ML and data operations within a regulated financial services firm. The role involves designing product features for ethical and compliant AI use, translating AI risk into requirements, and collaborating with various stakeholders to integrate governance practices into the AI/ML development lifecycle.

What you'd actually do

  1. Design and implement product features that apply evolving AI & data governance frameworks, policies, and procedures to ensure the ethical and responsible use of AI technologies across the organization, ensuring compliance with regulations, standards, guidelines, and emerging regulations.
  2. Leverage AI-assisted rapid prototyping techniques to validate product requirements, accelerating stakeholder alignment and product discovery.
  3. Support the product roadmap and backlog; author PRDs and acceptance criteria; define and track KPIs/OKRs to measure adoption, control coverage, lifecycle compliance and streamlined governance.
  4. Translate AI and data risk into produce requirements and control points embedded into the development lifecycle including model onboarding, model lineage and modification tracking, and agentic systems, data and model risk governance.
  5. Collaborate with cross-functional stakeholders such as Firmwide CDO, data scientists, engineers, legal, compliance, design and business units to integrate AI & Data governance practices into the AI/ML & data development lifecycle

Skills

Required

  • 3+ years of experience in product management, technology governance, risk management, or compliance within regulated industries such as financial services, with a focus on Artificial Intelligence
  • Basic understanding of AI/ML (including GenAI) and data governance, Models, MLOps, Agents, MCP, A2A and technology governance/risk/compliance principles.
  • Familiarity with using AI-assisted coding/prototyping tools (GitHub Copilot, Claude Code etc.) to product, iterate on prototypes, PRDs and technical concepts in close partnership with engineering and design.
  • Proven experience in business analysis to identify critical requirements by understanding complex and interdependent processes.
  • Strong critical thinking and problem-solving skills, with the ability to identify and mitigate risks effectively.
  • Excellent presentation and communication skills, with the ability to convey complex information to senior leaders and stakeholders.
  • Proven ability to collaborate effectively across cross-functional teams and build strong working relationships.

Nice to have

  • Experience with public cloud platforms (e.g., AWS, GCP, Azure) is a plus.
  • Demonstrated a track record of building end-to-end prototypes with AI assistance and translating them into engineering-ready acceptance criteria; strong experience with AI coding assistants.
  • Computer Science undergrad/grad degree and ideally experience working as an engineer in Financial Services with a focus on building AI products.
  • Certifications in AI governance, data governance, or related fields.

What the JD emphasized

  • AI/ML and data governance frameworks, policies, and procedures
  • ethical and compliant application of AI and data technologies
  • adhering to sustainable best practices in compliance with JPMC technology, operational risk, and relevant regulations
  • AI & data governance initiatives and ensure they align with regulatory requirements and industry best practices
  • AI & data risk into produce requirements and control points embedded into the development lifecycle including model onboarding, model lineage and modification tracking, and agentic systems, data and model risk governance
  • AI governance
  • data governance

Other signals

  • AI/ML and data governance frameworks, policies, and procedures
  • ethical and compliant application of AI and data technologies
  • adhering to sustainable best practices in compliance with JPMC technology, operational risk, and relevant regulations
  • AI & data governance initiatives and ensure they align with regulatory requirements and industry best practices
  • AI & data risk into produce requirements and control points embedded into the development lifecycle including model onboarding, model lineage and modification tracking, and agentic systems, data and model risk governance