Senior Associate -Applied AI Data Scientist

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 ke...