Data Scientist Associate

Role Overview

Shape the future of intelligent products by building production-ready generative AI services that create measurable impact. As a Data Scientist Associate at JPMorgan Chase, you will design, build, and operate production generative AI solutions that deliver measurable business outcomes by combining enterprise data with large language models and well-instrumented workflows.

Responsibilities

• Design production generative AI services that use enterprise data to solve defined business problems.
• Build large language model workflows including prompt orchestration, retrieval-based augmentation, and tool calling.
• Develop agent-based AI workflows with multi-step orchestration and state management.
• Own end-to-end delivery from solution design and data preparation through evaluation, deployment, and monitoring.
• Implement evaluation frameworks using offline test sets and automated regression testing.
• Apply data analysis techniques to diagnose model behavior and improve system performance.
• Build data pipelines using Python and SQL.
• Deliver containerized services to Kubernetes with automated CI/CD pipelines.
• Monitor service health and model performance using observability practices.
• Communicate progress and results to technical and non-technical stakeholders.

Requirements

• Bachelor's degree in computer science, data science, engineering, statistics, or a related quantitative field.
• Four or more years of hands-on experience using Python to build data-driven or machine learning-enabled solutions.
• Hands-on experience building generative AI applications using LLM APIs, advanced prompting, and RAG.
• Practical experience building multi-step, tool-using AI workflows using agent frameworks.
• Proficiency with relational databases and SQL (e.g., MySQL, Oracle, or PostgreSQL).
• Working knowledge of core machine learning concepts like regression, classification, and feature engineering.
• Proven experience designing and maintaining automated build, test, and deployment pipelines.
• Hands-on experience containerizing applications and deploying on Kubernetes.

Skills

• Python
• SQL
• Kubernetes
• Large Language Models
• Machine Learning

Nice to Have

• Experience operating production workloads on AWS.
• Working knowledge of front-end development such as React.
• Experience in regulated environments involving technology risk and compliance.
• Familiarity with model observability practices.