Senior Associate -Applied AI Data Scientist

JPMorgan Chase & Co. • Jersey City, NJ

7h

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

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.

Benefits

• We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location.
• Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.
• We also offer a range of benefits and programs to meet employee needs, based on eligibility.
• These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed