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, 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 non-technical audience.

As an Applied AI ML Data Scientist Lead-Vice President on 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.

Job 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 exercise and present them in appropriate format according to the audience.
Communicate AI/GenAI capabilities and results to both technical and non-technical audiences.
Stay informed about the latest trends and advancements in the latest 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 2+ years of relevant experience or MS/BS in Computer Science or a related field with 4+ 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 as well as practical experience with other languages such as R, Java and other equivalent languages.
Demonstrated experience working with large and complicated datasets.
Experience with ML frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
Experience integrating user feedback to establish agentic refinement and self-improving AI applications.
Solid understanding of fundamentals of statistics and machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning) and generative model architectures, particularly Transformer.
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 RAG and Agentic AI framework
Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.

Job 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 exercise and present them in appropriate format according to the audience.
Communicate AI/GenAI capabilities and results to both technical and non-technical audiences.
Stay informed about the latest trends and advancements in the latest 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 2+ years of relevant experience or MS/BS in Computer Science or a related field with 4+ 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 as well as practical experience with other languages such as R, Java and other equivalent languages.
Demonstrated experience working with large and complicated datasets.
Experience with ML frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
Experience integrating user feedback to establish agentic refinement and self-improving AI applications.
Solid understanding of fundamentals of statistics and machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning) and generative model architectures, particularly Transformer.
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 RAG and Agentic AI framework
Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.