What are the responsibilities and job description for the Executive Director Product Solutions Manager - Chief Data Analytics Office Fusion Platform position at JPMorgan Chase?
Unlock your potential as a leader in product solutions, where you will guide sales advisory, optimize complex problem-solving, and shape customer-centric strategies. Leverage your expertise to make a lasting impact in a fast-paced, collaborative environment. Within the Chief Data & Analytics Office (CDAO) at JPMorganChase, you will help accelerate the firm’s data and analytics journey by supporting high-quality, secure, and well-governed data. You will help harness artificial intelligence to improve productivity, enhance risk management, and enable new capabilities responsibly. This role sits at the intersection of strategy, product delivery, and partnership across business, technology, and control teams.
As a Product Solutions Director in CDAO (Chief Data & Analytics Office), Data Governance, you are an expert in a cluster of products and the sales cycle. As a leader on the team, you leverage your advanced capabilities to craft complex solutions, partner with Sales to identify and capture market opportunities, and create new ways for teams to continuously deliver value to customers. You will drive the development of strategy and roadmaps for data governance initiatives, including next-generation data governance tooling and AI-for-data capabilities. You will collaborate with stakeholders, subject matter experts, and engineers to define use cases, requirements, and dependencies, critically assessing proposed solutions. You will balance timeliness with quality under tight deadlines while managing multiple priorities and cross-functional partnerships.
The CDAO (Chief Data & Analytics Office) is responsible for ensuring the quality, integrity, and security of the company’s data and leveraging it to generate insights and drive decision-making. The organization also develops and implements solutions that support the firm’s commercial goals by harnessing artificial intelligence to develop new products, improve productivity, and enhance risk management effectively and responsibly. In this role, you will ensure end-to-end relevance to stakeholder needs, from gathering business requirements through successful delivery into production. You will communicate complex ideas effectively to collaborators and senior leaders using precise terminology and relatable examples. If you are passionate about data, data governance, and AI-for-data innovation, and enjoy learning and experimenting with new approaches, this role is for you.
Job responsibilities
- Advises the Product Solutions teams on solutioning and adopting new and existing client-facing products and capabilities while crafting complex solutions and assessing risk to enhance the customer experience
- Leverages extensive knowledge of a cluster of products and capabilities to manage the strategic development of end-to-end product solution strategies and processes
- Partners with Sales to advise on strategic pricing for deals, contributes to the development of sales training and collateral, and oversees Request for Proposal (RFP) responses
- Manages the collection of client feedback and oversees the delivery of feedback to Product teams
- Drives the development of strategy and roadmaps for data governance initiatives, including next-generation data governance tooling and AI-for-data capabilities
- Collaborates with stakeholders, subject matter experts, and engineers to understand use cases, requirements, and dependencies, critically assessing proposed solutions.
- Communicates complex ideas effectively to collaborators and senior leaders using precise terminology and relatable examples
- Balances timeliness with quality under tight deadlines, managing multiple priorities and cross-functional partnerships.
- Ensures end-to-end relevance to stakeholder needs, from gathering business requirements and working with technology teams to achieve successful delivery
- Defines and refines customer-centric solution approaches that connect data governance capabilities to measurable business outcomes.
- Partners with business, technology, and control stakeholders to deploy solutions into production effectively and responsibly
Required qualifications, capabilities, and skills
- 8 years of experience or equivalent expertise leading and developing solutions across multiple teams and a cluster of products
- Extensive experience facilitating sales cycle activities and developing and optimizing strategies and processes
- Demonstrable experience structuring and handling complex solutions for business problems to meet clients’ needs
- Understanding and hands-on experience building agentic AI systems within regulated or compliance-driven environments
- 8 years of experience developing enterprise-wide data, data governance, or AI strategy for large, complex organizations.
- 8 years of experience either as a product manager, product designer, engineer, data analyst, data scientist, or user researcher
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking, and detail-oriented, and motivated by complex analytical problems.
- Ability to collaborate effectively with stakeholders, subject matter experts, and engineers to translate needs into clear requirements
- Ability to balance quality and speed under tight deadlines while managing multiple priorities and partnerships.
- Ability to drive end-to-end delivery—from business requirements through implementation—ensuring outcomes meet stakeholder needs
Preferred qualifications, capabilities, and skills
- Direct experience with MCP (Model Context Protocol) designing tool schemas, building MCP servers, managing tool surface exposure, or integrating MCP into an agent platform
- Experience in regulated industries (financial services, healthcare, or government) with practical exposure to model risk management, audit trails, and compliance-driven engineering constraints.
- Familiarity with agent security concerns: prompt injection, tool misuse, over-privileged tool access, and blast radius containment strategies
- Experience building evaluation frameworks for LLM-based systems, including production-grade evaluation pipelines with structured outputs and regression tracking.
- Exposure to cloud-native AI infrastructure (managed model endpoints, model gateways, token/cost observability, and multi-tenant serving considerations)
- Experience contributing to developer-facing SDK or platform tooling (designing APIs, writing effective documentation, iterating based on adoption signals).
- Familiarity with responsible AI practices as they apply to agents, including human oversight requirements, escalation paths, intervention hooks, and auditability standards