Product Manager, AI Platform

JPMorgan Chase & Co.•Columbus, OH

15h

About The Position

You enjoy shaping the future of product innovation as a hands-on leader, driving measurable value for customers, guiding successful launches, and exceeding expectations. Join a dynamic Corporate & Investment Banking team to build and scale a production entity data platform that enables trusted decisions across front-office workflows, risk and controls, and AI-driven applications. As a Product Manager in the C360 team, you are an integral part of the organization that delivers core data products used every day across the firm. You will own the end-to-end product life cycle for resolving millions of organizational records from internal systems and third-party providers into a single, trusted global universe of entities, and producing an arbitrated “golden profile” that downstream platforms rely on. This is a product ownership role focused on shipping capabilities into production at scale, not an advisory or analytics-only role and requires strong operating discipline, technical fluency, and cross-functional leadership.

Requirements

5+ years of experience or equivalent expertise in product management or a relevant domain area
3+ years of owning complex data products or platforms where correctness, scale, and adoption are equally critical.
Demonstrated track record of shipping production products end-to-end, including roadmap ownership, backlog management, and measurable outcomes; experience delivering operationally supported platforms, not presentations.
Strong technical fluency across data platform fundamentals, including entity modeling, mastering and arbitration patterns, metadata and lineage, provenance, and data quality dimensions.
Ability to reason about algorithmic and operational trade-offs, including precision/recall, false positives/negatives, latency/throughput, and explainability versus automation, and to translate these into product decisions and success metrics.
Experience working with cross-functional teams across engineering, data engineering, applied machine learning, operations, and governance, with proven ability to influence in a matrixed environment.
Strong product operating discipline, including dependency management, release planning, clear requirements definition, and executive-level communication.

Nice To Haves

Demonstrated prior experience working in a highly matrixed, complex organization
Experience in financial services, particularly Corporate & Investment Banking, including exposure to enterprise data controls and audit expectations.
Prior experience with entity resolution or identity matching, deterministic rules frameworks, and machine learning-assisted matching or classification in high-volume environments.
Experience designing explainability, auditability, and human-in-the-loop governance patterns for AI-enabled production workflows.
Experience sourcing, normalizing, and integrating third-party data, including establishing scalable onboarding patterns and quality standards.
Familiarity with knowledge representation approaches such as knowledge graphs or ontology-driven modeling, particularly where downstream consumers require traceability and consistent semantics.

Responsibilities

Develops a product strategy and product vision that delivers customer value by establishing a single, trusted, global universe of organizations and an arbitrated “golden profile” that downstream teams and platforms can rely on in production.
Manages discovery efforts and market research to uncover customer solutions and integrate them into the product roadmap, including partnering with front-office, operations, and control stakeholders to define measurable outcomes for match quality, duplicate reduction, profile completeness, and adoption.
Owns, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition, translating business needs into clear, testable requirements for entity resolution, attribute arbitration, challenge-and-override workflows, and data onboarding patterns.
Builds the framework and tracks the product’s key success metrics such as cost, feature and functionality, risk posture, and reliability, including precision/recall and false positive/negative rates, resolution throughput and cycle time, duplicate creation rates, golden profile correctness and completeness, and service-level targets for adjudication workflows.
Leads delivery of entity resolution at scale across internal systems and third-party sources by balancing deterministic rules with machine learning-assisted matching, ensuring resolution decisions are explainable, traceable, and auditable for downstream reliance.
Owns the arbitration and “golden record” capabilities that select best attribute values using configurable logic (for example, consensus and recency), including workflows that allow expert challenge, override, and safe propagation of corrections with full provenance.
Defines a third-party data onboarding strategy and operating model, prioritizing integrations based on business value and readiness, setting quality and documentation standards, and establishing scalable onboarding patterns that prevent uncontrolled schema sprawl.
Delivers diagnostic and operational tooling that enables users and operators to understand why entities matched or did not match, how attribute selections were made, and where data quality issues are creating adverse outcomes.
Introduces AI- and agent-assisted processing patterns to improve throughput and reduce manual intervention, while maintaining appropriate governance, human-in-the-loop controls, and objective evaluation of model performance over time.
Partners closely with engineering, applied machine learning, architecture, data governance, and business stakeholders to manage dependencies, ensure resiliency and stability, and drive executive-ready communication on progress, risks, and trade-offs.