Personalization Product Associate - Senior Associate

JPMorgan Chase & Co.•New York, NY

15h•$99,750 - $150,000

About The Position

As a Senior Product Associate in the Personalization & Customer Insights Team, you are the day-to-day execution lead for the Customer Intelligence Hub. You will own feature delivery from discovery through production, manage the product backlog across engineering teams, run experiments to validate ranking models and summary quality, and partner closely with Data Scientists on model evaluation — including LLM-as-a-Judge quality scoring and contextual bandit optimization. You are responsible for translating strategy into sprint-level delivery that ships measurable customer value. The Customer Intelligence Hub is the personalized content generation layer — it takes what we know about a customer and generates what gets said to them and about them. But generation is only the starting point. The platform also discovers new moments that matter by mining customer data and behavioral patterns, proactively surfaces intelligence triggered by customer events (not just on-demand requests), predicts which insights will resonate before they reach a customer, and continuously learns from engagement signals to get sharper over time. Every generation emits natural language for humans and structured companion data for machines. Your consumers include bankers in branches, digital mobile and web surfaces, and more.

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
Experience shipping ML/AI-powered features to production in close partnership with Data Scientists and Engineers
Strong backlog management skills: JIRA epics, user stories, acceptance criteria, refinement, sprint planning, and delivery tracking
Data literacy: ability to read model metrics, interpret experiment results (A/B tests, statistical significance), and make prioritization decisions based on data
Experience working with Data Scientists on the model lifecycle — design, evaluation, deployment, and monitoring
Comfort with LLM-based products: prompt engineering concepts, quality evaluation methodologies, and output governance
Clear, structured communicator with strong written and presentation skills; ability to translate technical complexity into stakeholder-ready narratives
Proven ability to work across technical and non-technical teams — comfortable partnering with Data Scientists on model design and with marketing or product teams on use-case adoption

Nice To Haves

Familiarity with recommendation or ranking systems (contextual bandits, LinUCB, DLRM, embeddings)
Experience with LLM evaluation pipelines (LLM-as-a-Judge, quality rubrics, automated scoring)
Understanding of personalization at scale, particularly in financial services
Experience with real-time ML serving infrastructure (Ray Serve, streaming pipelines, Flink/Kafka, or equivalent)
Experience with API-first delivery on cloud (e.g., AWS) and coordination across multi-channel experiences (mobile, web, branch, contact center)
Demonstrated prior experience working in a highly matrixed, complex organization
BS or MS in Engineering, Data Science, Business, or a comparable field of study

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
Own sprint backlog end-to-end (epics, stories, refinement) and manage delivery across multiple engineering teams.
Lead ranking/selection and summary-quality testing (hypotheses, A/B design, measurement) with Data Science/ML partners.
Run a data-led pipeline to mine behavior, identify “moments that matter,” and ship ranked candidate insights every sprint.
Define triggers (events, account changes, thresholds) to push pre-computed insights to channels ahead of customer need.
Evolve a reusable generation platform; drive production hardening, compliance, and operational excellence with platform engineering.
Enable self-service discovery, partner with business/marketing, and track KPIs (cycle time, engagement lift, model performance, companion-data adoption, trigger coverage).