Senior Personalization Product Lead

JPMorgan Chase & Co.; New York (NY); Hybrid; USD 120,000 - 150,000; Full time

Job summary

JPMorgan Chase & Co. in New York hires a Senior Product Associate to lead day-to-day delivery for Customer Intelligence Hub.

You will own feature delivery from discovery through production, manage backlogs across engineering teams, run experiments to validate models, and collaborate with Data Scientists on model evaluation, including LLM-based quality scoring and contextual bandit tuning. You will translate strategy into sprint-ready plans, enabling the generation platform to deliver

Qualifications

• 3+ years of experience in product management or equivalent
• Proficient knowledge of the product development life cycle
• Experience with discovery and requirements definition
• Experience shipping ML/AI-powered features to production
• Strong backlog management and delivery tracking skills
• Ability to read model metrics and interpret experiment results

Responsibilities

• Partner with PM to identify new product opportunities via user research and discovery
• Plan upstream/downstream implications of new features
• Support user research, journey mapping, and market analysis for strategic roadmap
• Analyze product metrics across development life cycle
• Write requirements, epics, and user stories for development
• Own sprint backlog end-to-end across multiple engineering teams
• Lead ranking/selection and summary-quality testing with Data Science/ML teams
• Run data-led pipelines to mine behavior and surface insights each sprint
• Define triggers to push pre-computed insights to channels
• Drive production hardening, compliance, and platform governance

Skills

Product management

Backlog management

A/B testing

Data literacy

Cross-functional collab

Education

BS/MS in Engineering, Data Science, or Business

Tools

JIRA

Job description

JPMorgan Chase & Co. in New York hires a Senior Product Associate to lead day-to-day delivery for Customer Intelligence Hub.

You will own feature delivery from discovery through production, manage backlogs across engineering teams, run experiments to validate models, and collaborate with Data Scientists on model evaluation, including LLM-based quality scoring and contextual bandit tuning. You will translate strategy into sprint-ready plans, enabling the generation platform to deliver