Applied AI ML Data Scientist-Senior Associate

About the Role

The Legal Applied AI/ML team in Corporate Technology at JPMorgan Chase focuses on solving challenging business problems such as semantic search, question answering, document analysis, and automation of service inquiries through data science and ML techniques, particularly using GenAI and LLM tools and techniques. You will work with the firm’s rich data pool from both internal and external sources using GenAI tools and frameworks, Python/Spark via AWS and other systems. You are also expected to derive business insights from technical results and be able to present them to a non‑technical audience.

As an Applied AI/ML Data Scientist – Senior Associate on the Corporate team, you will have the opportunity to study complex business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models for those problems.

Responsibilities

• Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
• Develop GenAI and LLM solutions to solve business problems.
• Implement optimization strategies to fine‑tune generative models for specific GenAI use cases, ensuring high‑quality outputs.
• Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
• Generate structured and meaningful insights from data analysis and modelling exercises and present them in an appropriate format for the audience.
• Communicate AI/GenAI capabilities and results to both technical and non‑technical audiences.
• Stay informed about the latest trends and advancements in AI research, implement cutting‑edge techniques, and leverage external APIs for enhanced functionality.

Required Qualifications, Capabilities, and Skills

• PhD in Computer Science or a related quantitative discipline with 1+ years of relevant experience or MS/BS in Computer Science or a related field with 2+ years of relevant experience.
• Practical expertise with LLM projects as well as other supervised and unsupervised techniques; proven track record of deploying AI/ML applications in a production environment.
• Proficient programming skills with Python and SQL, and practical experience with other languages such as R, Java, or equivalent.
• Demonstrated experience working with large and complicated datasets.
• Experience with ML frameworks, libraries, and APIs (e.g., TensorFlow, PyTorch, Scikit‑learn, OpenAI API).
• Experience integrating user feedback to establish agentic refinement and self‑improving AI applications.
• Solid understanding of fundamentals of statistics and machine learning (classification, regression, time series, deep learning, reinforcement learning) and generative model architectures, particularly Transformers.
• Ability to identify and address AI/LLM challenges, implement optimizations, and tune models for optimal performance in NLP applications.
• Excellent problem‑solving, communication (verbal and written), and teamwork skills.

Preferred Qualifications, Capabilities, and Skills

• Experience working with engineering teams to operationalize ML models.
• Expertise in designing and implementing pipelines using Retrieval‑Augmented Generation (RAG) and Agentic AI frameworks.
• Hands‑on knowledge of Chain‑of‑Thoughts, Tree‑of‑Thoughts, and Graph‑of‑Thoughts prompting strategies.