Product Manager – Personalization & Customer Insights (ML Product Portfolio)

Shape how millions of customers experience smarter, more relevant interactions. In this role, you’ll lead a portfolio that turns insights into timely, personalized actions across key moments that matter. You’ll bring together product, design, engineering, and data science to deliver measurable outcomes with trust at the center. Join us to build responsible innovation that improves customer experiences and helps teams move faster. As a Product Manager in Personalization & Customer Insights, you will own the vision, strategy, and end-to-end lifecycle for a portfolio of machine-learning (ML) products that transform customer data into actionable intelligence. You’ll represent customer needs, translate research and analytics into a clear roadmap, and align cross-functional partners across a highly matrixed organization. You’ll help deliver proactive, personalized intelligence and next-best-action experiences across key moments such as marketing, servicing, conversational assistants, and personal financial management. You’ll also enable partner teams to self-serve personalization and insights for their own use cases through shared capabilities and foundations. Along the way, you’ll uphold privacy, consent, and fairness standards while guiding scalable experimentation and reliable delivery. Job responsibilities - Develop a product vision and strategy that delivers measurable value to customers and partners - Lead cross-functional execution across Product, Design, Engineering, Marketing/Servicing, and business stakeholders in a highly matrixed environment - Own the roadmap and backlog for a portfolio spanning recommendations, ranking, predictive models, segmentation, assistant experiences, and the model development lifecycle platform - Lead discovery and research, translating insights into epics, user stories, and acceptance criteria - Design hypotheses and run experiments (such as A/B tests) to validate impact and measure outcomes - Partner with data scientists and machine learning engineers across the model lifecycle from problem framing through production release - Collaborate with Risk, Privacy, and Compliance to guide consent, governance, and fairness practices - Define and track key performance indicators tied to engagement, quality, cost, risk posture, reliability, and business outcomes - Champion application programming interface and event-driven delivery patterns on cloud infrastructure to enable scalable signals and context across channels - Uphold responsible artificial intelligence (AI) and data practices, including consent management, fairness, and governance - Cultivate a culture of experimentation, measurable outcomes, and responsible innovation Required qualifications, skills, and capabilities - 8 years or more of product management experience (or equivalent), including ownership of product vision and roadmap for a portfolio - Experience delivering artificial intelligence or machine-learning products (AI/ML) into production for customer-facing experiences - Experience with personalization or next-best-action use cases, including experimentation and rigorous outcome measurement - Demonstrated ability to translate research, analytics, and metrics into roadmap decisions balancing time, cost, and quality - Hands-on data fluency, including ability to explore and query data using SQL or Python and JPMorganChase-approved developer tools - Experience leading work in ambiguous, rapidly evolving problem spaces and turning uncertainty into clear plans and priorities - Experience aligning and influencing cross-functional stakeholders (Product, Design, Engineering, Data Science, and business leaders) in a highly matrixed organization - Experience defining, tracking, and communicating measurable outcomes and delivery health using agreed success metrics - Experience writing product artifacts such as epics, user stories, and acceptance criteria to guide delivery Preferred qualifications - Production experience with AI/ML and large language model solutions, including evaluation, iteration, and responsible deployment - Experience building or shipping platforms and data products that enable model developers (context, signals, evaluation, and self-service tooling) - Bachelor’s or Master’s degree in Engineering, Data Science, or a comparable field - Familiarity with digital banking trends and privacy, consent, and fairness considerations in artificial intelligence (AI)