Data Scientist [Multiple Positions Available]

JPMorgan Chase & Co.•New York, NY

1d•$160,000 - $170,000•Onsite

About The Position

JPMorgan Chase is seeking Data Scientists to develop deep learning frameworks for NLP tasks aligned with business requirements. The role involves creating technical solutions using large language models for content extraction, search, question answering, reasoning, and recommendation. Responsibilities include building testing setups to evaluate model performance, monitoring and improving models, presenting results to stakeholders, and collaborating with engineering and product teams for production deployment. The position also requires studying scientific literature to identify and implement state-of-the-art NLP techniques.

Requirements

Master's degree in Business Analytics, Data Science, Computer Science, Machine Learning, Artificial Intelligence, or related field of study.
3 years of experience in the job offered or as Data Scientist, Machine Learning Engineer, or related occupation.
Three (3) years of experience with Designing AI/ML models for Natural Language Processing (NLP) solutions using programming languages such as Python or Java and Machine Learning libraries and frameworks such as Torch or Huggingface.
One (1) year of experience with Building LLM-based solutions including prompt and context engineering, retrieval augmented generation (RAG), tool calling and agent orchestration.
One (1) year of experience with Analyzing financial reports, analysts notes, client communications to extract actionable insights and empower data-driven decision making in financial markets.

Responsibilities

Study, design, and develop deep learning frameworks for NLP tasks aligned with business requirements.
Develop technical solutions utilizing large language models for a variety of problems including content extraction, search and question answering, reasoning and recommendation.
Build testing setups to evaluate model performances and ensure the efficacy and reliability of the LLM solutions.
Monitor and improve model performance through feedback and active learning.
Deliver written, visual, and oral presentation of modeling results to business stakeholders.
Collaborate with engineering and product teams to deploy scalable solutions in production.
Study scientific articles and research papers to identify emerging and state-of-the-art NLP techniques and discover new approaches.