Senior Associate Applied AI Data Scientist

About the role JPMorgan Chases 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 retrievalaugmented generation (RAG) tool use and multistep 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) humanintheloop reviews redteam 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 highvalue 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; handson experience with LLMs.
• Strong Python and SQL.
• Practical knowledge of RAG prompt engineering finetuning 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 multiagent 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 infrastructureascode.

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.