Personalization Product Manager - Data Platform

JPMorgan Chase & Co.; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Product Manager to lead personalization initiatives, translating user research and analytics into actionable roadmaps and epics.

You will collaborate with data science, ML engineering, and channel teams to prioritize opportunities and design scalable, privacy-conscious product features. You will guide experiments, measure impact, and ensure delivery milestones across mobile, web, contact centers, branches, and marketing channels.

Qualifications

• 5+ years of product management experience.
• Experience with discovery and requirements definition.
• Ability to query data sets (SQL, Python).
• Experience delivering AI-powered products with data science teams.
• Familiarity with Jira and modern data platforms.

Responsibilities

• Partners with the Product Manager to identify new product opportunities that reflect customer needs.
• Translates research and analytics into epics, user stories, and acceptance criteria.
• Lead ML-powered personalization and next-best-action initiatives across channels.
• Coordinate with data scientists and engineers across design, training, evaluation, deployment, and monitoring.
• Define and track product metrics across the lifecycle.

Skills

Product management

Data analytics

SQL

Python

AWS data lakes

Snowflake

Jira

Experiment design

Tools

Tableau

Grafana

Job description

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 Product Manager within the Personalization and Insights Team, you contribute to the team by leveraging your expertise in product development and optimization to make a significant impact, supported by user research and customer feedback to fuel the creation of innovative products and continuously improve existing offerings. Collaborate closely with cross-functional teams and play a crucial role in shaping the future of our products and ongoing success.

You will lead the strategy and execution of machine learning–powered personalization and next-best-action capabilities across mobile, web, contact center, branch, and marketing. You will translate research and analytics into clear roadmaps, partner with data science, machine learning engineering, channel teams, and business stakeholders to prioritize opportunities, design and iterate recommendation, next-best-action, and agentic solutions, run experiments to validate impact, and operationalize scalable, reliable model lifecycle infrastructure—while upholding privacy, consent, and fairness standards.

Job 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.
• Partner with Channel/Interface teams to define how assistant memory and context are used, and help align on contracts, service levels, and guardrails for safe, reliable experiences.
• Support machine learning–powered personalization and next-best-action initiatives by helping frame hypotheses, document use cases, and assist with experiment design and impact measurement.
• Translate user research and analytics into epics, user stories, and acceptance criteria, and manage day-to-day backlog grooming and sprint execution across discovery through launch.
• Coordinate with data scientists and engineers across design, training, evaluation, deployment, and monitoring to ensure requirements are clear and delivery milestones are met.
• Contribute to an application programming interface– and event-driven cloud approach by capturing requirements for context/signals and tracking product metrics (engagement, quality, outcomes).
• Follow responsible data practices with risk, privacy, and compliance partners (consent, fairness), and support team ways of working through documentation and continuous improvement.

Required qualifications, capabilities, and skills

• 5+ 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.
• Working knowledge of data analytics and data literacy.Familiarity with AWS data lakes, Snowflake, and modern data platforms.
• Deliver customer-facing artificial intelligence products with data science and engineering teams across the model lifecycle.
• Build data products for agentic assistants (memory, context, signals) and integrate with channel experience teams.
• Deliver personalization and next-best-action for marketing/servicing using experiments and outcome measurement.
• Execute core product practices: discovery, requirements, and backlog management in Jira.
• Use research and metrics to drive roadmaps and deliver on time, cost, and quality.
• Collaborate and influence across product, design, engineering, and business stakeholders.
• Ability to understand and query data sets for analysis or requirements gathering (e.g., using SQL, Python)

Preferred qualifications, capabilities, and skills

• Experience supporting personalization and decisioning products (recommendations/next-best-action) with hands-on experiment design and measurement.
• Familiarity with agentic assistant patterns (memory, context, signals) and data products enabling multi-channel customer experiences.
• Working knowledge of application programming interface–first, event-driven architectures on cloud platforms and production monitoring fundamentals.
• Understanding of responsible data and model practices (privacy, consent, fairness) in regulated environments.
• Exposure to machine learning or data science concepts
• Experience with tools such as Tableau, Grafana, or similar for documentation and data visualization