Senior Product Associate, C360

JPMorgan Chase & Co.•New York, NY

9d•$99,750 - $150,000

About The Position

Unleash your expertise in product development and optimization by leveraging user research, analyzing metrics, and collaborating across one of the world’s most innovative financial organizations. As a Senior Product Associate in Client360, you play a key role in delivering core data products used across the firm every day. You will own the end-to-end product lifecycle for Financials capabilities—integrating data from internal product platforms and third-party providers into a single, trusted financial reporting layer—and delivering a conformed “silver profile” that downstream platforms rely on. This is a hands-on product role focused on shipping scalable, production-ready capabilities (not an advisory or analytics-only role) and requires strong operating discipline, technical fluency, and cross-functional leadership. As part of Client360, you will champion user-centric service delivery, maximize the value and usability of data and AI solutions, and drive business satisfaction across the Commercial & Investment Bank (CIB).

Requirements

3+ years of experience or equivalent expertise in product management or a relevant domain area
Proficient knowledge of the product development life cycle
Experience in product life cycle activities including discovery and requirements definition
Developing knowledge of data analytics and data literacy
Knowledge of the firm’s business landscape, including how the CIB engages clients across products and services.
Proven experience delivering complex, cross-functional initiatives spanning data analysis, financial reporting, and technology delivery.
Demonstrated ability to document operating models/processes and drive change through strong partnerships and stakeholder management.
Strong analytical toolkit and agile mindset; ability to query and analyze data using tools/languages such as SQL (and platforms such as Databricks; tools such as Alteryx, where applicable).
Execution mindset with high attention to detail and ability to deliver against defined requirements.
Strong problem-solving skills with ability to synthesize large datasets into clear insights and crisp messaging.
Strong written and verbal communication skills with ability to tailor messaging to different audiences; proven ability to hold partners accountable for quality deliverables.

Nice To Haves

Experience working in a highly matrixed, complex organization.
Extensive experience in Data management, data operations, or CRM systems within a financial institution.
Advanced degree in Computer Science, Data Science, Business, or a related field.

Responsibilities

Partners with the Product Manager to identify new product opportunities that reflect the needs of our customers and the market through user research and discovery
Considers and plans for upstream and downstream implications of new product features on the overall product experience
Supports the collection of user research, journey mapping, and market analysis to inform the strategic product roadmap and provide insight on potential product features that provide value to customers
Analyzes, tracks, and evaluates product metrics including work to time, cost, and quality targets across the product development life cycle
Writes the requirements, epics, and user stories to support product development
Collaborate with technology leads and engineers on solution design; produce lightweight requirements/design artifacts to enable implementation.
Support agile ceremonies (refinement, sprint planning, demos, retros) and work day-to-day with scrum teams to drive execution and remove blockers.
Coordinate with upstream and downstream application/data teams to ensure end-to-end alignment, data quality, controls, and operational readiness.
Build and validate SQL-based reporting and prototypes to support analysis, performance measurement, and troubleshooting of data issues.
Leverage enterprise-authorized AI capabilities to accelerate analysis and documentation while validating outputs and handling data in accordance with sensitivity, security, and governance requirements.
Apply reuse-first, AI-assisted SDLC practices to strengthen requirement quality and data pipeline quality (e.g., test/control evidence generation), supporting traceability, auditability, resiliency, and security expectations