Product Manager - Vice President

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Consumer & Community Banking

Product Manager (VP) at JPMorgan Chase, focused on Business Banking. The role involves defining product strategy and vision, leading discovery for AI opportunities, managing the product backlog, tracking key metrics, and ensuring compliance. Requires hands-on experience with deploying Data Science and ML capabilities in production, with preferred knowledge of LLM architectures, fine-tuning, RAG, and agentic frameworks, as well as experience with evaluations.

What you'd actually do

  1. Lead discovery and use-case prioritization: identify high-value AI opportunities, size impact/feasibility, and maintain a ranked use-case pipeline.
  2. Owns, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition
  3. Builds the framework and tracks the product's key success metrics such as cost, feature and functionality, risk posture, and reliability
  4. Risk, controls, and governance: ensure the product meets policy/regulatory expectations; manage issues, operational risk, and audit readiness.
  5. Cross-functional stakeholder leadership: align business, operations, technology, risk, legal, compliance, and data governance; communicate trade-offs and decisions clearly

Skills

Required

  • 5+ years of experience or equivalent expertise in product management or a relevant domain area
  • Advanced knowledge of the product development life cycle, design, and data analytics
  • Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
  • Ability to work on tasks and projects through completion with limited supervision. Passion for detail and follow through
  • Effective verbal and written communication skills with technical and business audiences
  • Hands-on experience developing and deploying Data Science and ML capabilities in production at scale

Nice to have

  • Demonstrated prior experience working in a highly matrixed, complex organization
  • Good understanding of AI model architectures, such as Large Language Models, and methods such as prompting, context engineering, fine-tuning, RAG, MCPs, and agentic frameworks.
  • Experience defining robust evaluation sets and leading teams through quantitative and qualitative evaluations and iterations to achieve reliable, high-quality delivery.
  • Proven track record of delivering and launching successful products at scale.

What the JD emphasized

  • Hands-on experience developing and deploying Data Science and ML capabilities in production at scale
  • Good understanding of AI model architectures, such as Large Language Models, and methods such as prompting, context engineering, fine-tuning, RAG, MCPs, and agentic frameworks.
  • Experience defining robust evaluation sets and leading teams through quantitative and qualitative evaluations and iterations to achieve reliable, high-quality delivery.

Other signals

  • AI opportunities
  • Data Science and ML capabilities in production
  • AI model architectures
  • Large Language Models
  • prompting
  • context engineering
  • fine-tuning
  • RAG
  • agentic frameworks
  • evaluation sets
  • quantitative and qualitative evaluations