The role
From JPMorgan Chase's own posting.
Shape the future of AI-enabled banking by building the products that make enterprise data understandable, reusable, and actionable at scale. Join a highly visible team driving foundational capabilities that enable trusted, context-aware experiences across Consumer & Community Banking.
As a Product Manager within the Consumer & Community Banking Data & Analytics team, you are responsible for defining and delivering products that create and manage shared business context across the enterprise. You will lead the development of context-layer capabilities that enable data, knowledge, controls, and business meaning to be consumed consistently by analytics, AI, and agentic solutions.
Leveraging your expertise in product management, data platforms, and enterprise-scale transformation, you will partner closely with ontology, semantic engineering, technology, governance, and business teams to deliver products that improve interoperability, enable AI adoption, and strengthen customer and business outcomes. You will own product strategy, roadmap prioritization, stakeholder alignment, and measurable outcomes while ensuring solutions are scalable, resilient, governed, and trusted.
Job Responsibilities
Develop and execute product strategies and roadmaps for context-layer capabilities that support AI, analytics, and enterprise data initiatives.
Partner with ontology, semantic engineering, data, and technology teams to define requirements and deliver context products that enable agent-ready experiences.
Own and prioritize product backlogs, balancing customer needs, business objectives, technology feasibility, and regulatory requirements.
Drive adoption of enterprise ontologies, semantic models, and taxonomies across business domains.
Define and deliver context engineering tools, APIs, and reusable capabilities that accelerate AI and agent deployment across CCB.
Establish product success metrics and monitor outcomes related to adoption, semantic quality, operational efficiency, controls effectiveness, and business impact.
Partner with Risk, Compliance, Legal, Controls, and Data Governance teams to ensure products operate within approved policies and regulatory requirements.
Communicate product vision, progress, risks, and outcomes to senior leaders, stakeholders, and cross-functional teams.
Lead and mentor product team members while fostering a culture of innovation, accountability, continuous improvement, and customer-centric thinking.
Required Qualifications, Capabilities, and Skills
5+ years of experience or equivalent expertise in product management, data platforms, analytics, AI/ML products, or a related domain.
Advanced knowledge of the product development life cycle, agile practices, design thinking, and data-driven product management.
Demonstrated experience developing product strategies, roadmaps, business cases, and measurable success metrics.
Strong understanding of enterprise data management, semantic technologies, ontologies, taxonomies, knowledge graphs, or metadata management concepts.
Experience translating complex business and technical requirements into scalable product capabilities and customer outcomes.
Knowledge of governance, lineage, controls, auditability, privacy, and risk management principles within regulated environments.
Proven ability to influence cross-functional stakeholders and drive outcomes across large, matrixed organizations.
Strong analytical, problem-solving, communication, and executive presentation skills.
Experience managing competing priorities and delivering products with measurable business impact.
Ability to leverage AI-enabled insights and emerging technologies to improve product decision-making and business outcomes.
Preferred Qualifications, Capabilities, and Skills
Advanced degree in Computer Science, Information Science, Data Analytics, Engineering, Business, or a related discipline.
Experience within financial services or other highly regulated industries.
Experience delivering AI, machine learning, generative AI, or agentic AI-enabled products.
Familiarity with knowledge graph technologies, semantic web standards, ontology governance, and metadata management platforms.
Experience with virtual knowledge graph architectures and enterprise-scale data integration solutions.
Knowledge of retrieval-augmented generation (RAG), context engineering, memory frameworks, or reusable AI service architectures.
Experience leading large-scale transformation initiatives involving data, AI, analytics, or enterprise platforms.