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 Associate Product at JPMorgan Chase as part of the Employee Experience function you will work with cross-functional teams to deliver AI-enabled experiences and internal capabilities across the product portfolio. You will identify, define, prototype, and launch product improvements that solve real user and business problems, continuously improving existing offerings while exploring new opportunities that reflect the needs of our customers. You will operate with a builder mindset, using AI to accelerate core PM workflows while staying deeply engaged in the practical work of shaping solutions with engineers and data scientists. You will craft clear product intent, requirements, and evaluation criteria that align probabilistic model behavior with user needs, operational constraints, and appropriate
Job responsibilities
- Identify and prioritize product opportunities through user research, discovery, and market analysis, translating needs into clear problem statements and measurable outcomes while considering upstream and downstream implications on the overall product experience.
- Run lean discovery and validation loops, moving quickly from insight to prototype to shipped product, building hands-on alongside engineering, data science, and design rather than only producing documentation.
- Leverage AI as a core toolset and thinking partner across the product lifecycle, synthesizing research, drafting specs, analyzing metrics, generating prototypes, and stress-testing assumptions, while retaining accountability and sound judgment for all decisions.
- Write clear requirements, epics, user stories, and acceptance criteria that reduce ambiguity and enable teams to execute with speed, correctness, and appropriate quality standards.
- Analyze, track, and evaluate product metrics tied to user outcomes and operational targets, including time, cost, and quality benchmarks across the product development lifecycle.
- Bring practical AI fluency to product decisions, understanding how LLM applications, RAG systems, and agent patterns behave in production, to inform evaluation approaches, safety guardrails, and human oversight where needed.
Required Qualifications, Capabilities, and Skills
- You have 4+ years of product management experience delivering software products from concept to production, with demonstrated ownership of outcomes. You thrive as a hands-on problem solver who works effectively across functions.
- You have a working systems-level understanding of LLM application concepts including prompting, context windows, RAG fundamentals, embeddings and retrieval, and tool or function calling and agent patterns.
- You partner effectively with engineering and data science to define and interpret AI quality signals, evaluation approaches, test plans, launch criteria, and monitoring needs.
- You are data fluent, able to define success metrics, interpret dashboards and experiments, and connect probabilistic system performance to user and operational outcomes.
- You communicate with precision, writing specifications that reduce ambiguity while preserving speed, and you are comfortable making tradeoffs with incomplete information and articulating them clearly.
- You collaborate well across functions, align teams around decisions, and maintain high standards for controls, user experience, and operational excellence.
Preferred Qualifications, Capabilities, and Skills
- Experience shipping AI or ML-powered product features with measurable improvements to user outcomes or business performance.
- Hands-on experience with LLMs, RAG architectures, prompt engineering, and evaluation frameworks including golden datasets, human review workflows, and monitoring approaches.
- Demonstrated experience rapidly prototyping and iterating on AI features with a strong experimentation cadence and the ability to translate learnings into product decisions quickly.
- Familiarity with responsible AI practices including bias and fairness considerations, transparency, governance, and auditability.
- Experience uplifting team AI fluency and driving adoption of AI tools through playbooks, training, working norms, and repeatable practices