JPMorgan Chase
Vice President, Product Manager - Client 360 World Model & Ontology
450 W 33rd St, New York, NY 10001
Full-time
First seen by offpeak October 21, 2026
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As a Product Manager in C360 - World Model, you will be the hands-on owner of the World Model as a product — accountable for its structure, coherence, quality, and direction — delivering not by running a large team, but by coordinating the specialists who own each domain and the engineers who build the platform beneath it.
The World Model is the governed knowledge layer beneath that view: a version-controlled library of definitions — the entities C360 recognises, their properties, relationships, and data lineage — maintained as structured content, published to an interactive web application, and consumed directly by C360's production AI assistants through a subscription model. In short, it is the shared vocabulary that keeps every team, dataset, and AI agent describing the client the same way. Job responsibilities
- Develops a product strategy and product vision that delivers value to customers
- Manages discovery efforts and market research to uncover customer solutions and integrate them into the product roadmap
- Owns, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition
- Builds the framework and tracks the product's key success metrics such as cost, feature and functionality, risk posture, and reliability
- Own and maintain the enterprise data model, ensuring consistency, accuracy, and alignment across core client entities, including client identity, hierarchies, coverage roles, revenue metrics, and relationship measures. Resolve overlaps, conflicts, and gaps to deliver a unified product experience.
- Govern the change management process for the data model by managing a streamlined approval workflow, coordinating reviews, securing domain-owner sign-off, validating source references, and driving timely publication of approved updates.
- Establish and enforce data quality standards, reference integrity requirements, and content governance practices that support AI-enabled experiences. Partner with engineering teams to automate quality controls and embed validation checks into development and deployment pipelines.
- Manage and communicate the data consumption contract for AI assistants and downstream consumers, ensuring subscription accuracy, maintaining compatibility, and proactively coordinating breaking changes and release communications.
- Define and execute a focused roadmap for the data model product, prioritizing high-impact initiatives, balancing delivery against available capacity, and appropriately deferring or delegating lower-priority work.
- Collaborate across business and domain teams to align requirements, drive cross-functional decision-making, and escalate complex or disputed issues through established governance forums when necessary.
- Monitor and report on model health and operational performance through a consistent set of metrics, including data currency, reference integrity, backlog management, publication turnaround times, and subscription health.
- 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
- Product management or product ownership experience for a platform, data, or developer-facing product used by other teams — and comfort being its single, hands-on owner.
- A strong grasp of taxonomy, ontology, or data-model design: defining entities, relationships, and controlled vocabularies and keeping them consistent as they grow.
- Experience running a lightweight content or change workflow (intake → review → publish → retire) and getting things agreed through people you do not manage.
- A solid understanding of data lineage and provenance — the difference between a system of record and derived data, and why it matters for downstream trust.
- Comfort in a version-controlled, documentation-as-code environment (Git, structured Markdown / YAML, automated checks, pull-request review).
- Excellent prioritization and self-management — able to protect a focused roadmap and say no under real capacity limits.
- Clear, precise written communication — equally credible with engineers and with senior business stakeholders.
- Demonstrated prior experience working in a highly matrixed, complex organization
- Experience in wholesale / CIB banking (Corporates, Financial Institutions, Commercial Real Estate, Public Sector) or the businesses C360 serves (Global Banking, Markets, Payments, Securities Services).
- Familiarity with client-data domains — client identity and hierarchies, coverage models, revenue and wallet share, and relationship analytics.
- An understanding of how AI / LLM agents consume a knowledge or semantic layer (retrieval-augmented generation, tool / MCP integration) and how content quality shapes agent behaviour.
- Exposure to knowledge-graph or ontology tooling (for example RDF / OWL, SKOS, taxonomy managers) or GraphQL.
- Experience working alongside a data-governance function or council.