About the role JPMorgan Chase’s Asset & Wealth Management Finance organization is building the next generation of agentic AI solutions that act as “digital workers” for forecasting, analytics, and decision support. 

As a Senior Data Science Associate, you will design, deploy, and scale large language model (LLM) agents that turn complex finance questions into trusted, actionable insights.

Job responsibilities

Build production LLM agents for finance workflows using techniques such as retrieval‑augmented generation (RAG), tool use, and multi‑step reasoning.Develop robust data and inference pipelines in Python and SQL; integrate agents with APIs, microservices, and BI applications.Implement evaluation frameworks and guardrails: offline and online tests, automatic metrics (factuality, grounding, hallucination rate), human‑in‑the‑loop reviews, red‑team testing, and observability.Optimize for scale, latency, and cost across cloud environments; leverage vector databases and embeddings for efficient retrieval.Partner with Finance, Product, and Engineering to identify high‑value use cases; translate ambiguous problems into measurable outcomes.Apply solid ML engineering and MLOps practices (versioning, CI/CD, model registry, monitoring, incident response).Document systems, deliver enablement materials, and upskill partners; contribute to standards for privacy, security, and model risk governance.

Required qualifications, capabilities and skills

6+ years in data/ML roles, including 3+ years building and operating production ML applications; hands‑on experience with LLMs.Strong Python and SQL.Practical knowledge of RAG, prompt engineering, fine‑tuning, function/tool calling, and vector stores.Experience with cloud platforms (e.g., AWS, Azure, or GCP) and modern data stacks (e.g., Databricks or Snowflake).Familiarity with LLM frameworks and orchestration (e.g., LangChain or LlamaIndex) and REST/GraphQL API design.Proficiency in analytics and applied statistics; ability to design experiments and evaluate business impact.Excellent communication and stakeholder management; comfort working across Finance, Technology, and Operations.

Preferred qualifications, capabilities and skills

Experience building multi‑agent systems, autonomous workflows, or task planners.Eexperience with PySpark or distributed compute.Knowledge of model safety, bias, and privacy techniques; experience with model risk management and governance.Exposure to observability tools (logging, tracing, telemetry) and A/B testing.Background integrating agents with BI/reporting and workflow tools; familiarity with Tableau or similar is a plus.Experience with GPUs/accelerators, containerization, and infrastructure‑as‑code.

What success looks like

90 days: deliver a pilot finance agent with RAG and evaluation metrics, integrated with key data sources and APIs.6 months: scale agents across multiple workflows, establish guardrails and monitoring, and demonstrate clear improvements in cycle time, accuracy, or user satisfaction.