Vice President - Data Scientist Lead (LLM/GenAI)

JPMorgan Chase & Co.; Chicago (IL); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorgan Chase & Co. in Chicago seeks a Vice President, Data Scientist Lead to drive data-driven initiatives and deliver large language model-powered solutions for content extraction, enterprise search, and decision support.

You will partner with engineering and product teams to deploy scalable, governed AI capabilities using Bedrock and Cortex in a production environment. You will guide evaluation, guardrails, and MLOps while mentoring junior data scientists and translating complex business

Qualifications

• Advanced degree in Data Science, Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience
• 5+ years of applied experience building machine learning or natural language processing solutions, including production deployment in a fast-paced environment
• Experience with natural language processing and large language models, including prompt engineering, retrieval-augmented generation, and evaluation methodologies
• Hands-on experience with Amazon Bedrock or an equivalent managed large language model platform for building and deploying generative artificial intelligence solutions
• Experience with Cortex, including Cortex Analyst or related workflows, in an enterprise setting
• Strong Python skills and familiarity with machine learning or deep learning frameworks such as PyTorch or TensorFlow, and standard machine learning tooling such as pandas, NumNum, and scikit-learn
• Experience building application programming interfaces and integrating large language model or natural language processing solutions into applications and services
• Experience building data pipelines for structured and unstructured data processing, with understanding of embeddings, vector search, indexing, and retrieval patterns
• Experience with software engineering practices, including Git or version control, code quality, testing, and continuous integration and continuous delivery fundamentals
• Strong communication and stakeholder management skills with the ability to present tradeoffs, risks, and results concisely
• Strong analytical skills and working knowledge of financial services, markets, or asset management concepts

Responsibilities

• Develop and deliver generative artificial intelligence and large language model solutions for content extraction, semantic search, question answering, summarization, reasoning, and recommendation use cases
• Design, deploy, and manage prompt-based and retrieval-augmented generation systems, including orchestration patterns and agentic workflows such as tool use, structured outputs, and multi-step reasoning
• Build evaluation and testing frameworks to measure accuracy, faithfulness, robustness, latency, and cost, including red-teaming and safety checks where applicable
• Leverage Amazon Bedrock to prototype and productionize large language model applications, including model selection, prompt templates, routing, and deployment patterns
• Work hands-on with Cortex, including Cortex Analyst, to enable governed analytics experiences and generative artificial intelligence-assisted workflows
• Apply hands-on experience in environments such as Amazon Bedrock, Amazon SageMaker, or Databricks
• Collaborate with engineering teams to deliver scalable services, including application programming interfaces, batch jobs, and pipelines, while ensuring strong software engineering discipline and operational readiness
• Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for large language model applications
• Conduct applied research by studying scientific articles and current techniques in prompting, fine-tuning, evaluation, and agent design, then translating them into practical improvements
• Communicate clearly with technical and non-technical stakeholders by translating business needs into measurable problem statements, solution designs, and success metrics
• Mentor junior data scientists, influence standards, and drive adoption of responsible artificial intelligence practices

Skills

Python

NLP/LLMs

Large Language Models

Amazon Bedrock

Cortex

Data Pipelines

Retrieval-Augmented Generation

Prompt Engineering

Model Evaluation

Vector Search

Education

Advanced degree in Data Science

Advanced degree in Computer Science

Advanced degree in Machine Learning

Advanced degree in Statistics

Tools

PyTorch

TensorFlow

pandas

NumPy

scikit-learn

Git

Job description

Join the Global Services Insights & Analytics team and help transform data into actionable insights. You will collaborate with senior leaders and cross-functional partners to improve operational performance, efficiency, service, and controls. This role offers the opportunity to lead high-impact generative artificial intelligence solutions across financial services use cases while helping deliver reliable, scalable, and governed capabilities.

As a Vice President, Data Scientist Lead in the Global Services Insights & Analytics team, you will lead data-driven initiatives that enhance planning, efficiency, service, and controls within Commercial Banking. You will design and deliver large language model-powered solutions across high-impact use cases, including content extraction, enterprise search and question answering, reasoning, summarization, and recommendations. You will partner closely with engineering and product teams to deploy reliable, scalable, and governed generative artificial intelligence capabilities using Amazon Bedrock and Cortex platforms. Your work will emphasize evaluation, guardrails, and production-grade machine learning operations.

Job Responsibilities

• Develop and deliver generative artificial intelligence and large language model solutions for content extraction, semantic search, question answering, summarization, reasoning, and recommendation use cases
• Design, deploy, and manage prompt-based and retrieval-augmented generation systems, including orchestration patterns and agentic workflows such as tool use, structured outputs, and multi-step reasoning
• Build evaluation and testing frameworks to measure accuracy, faithfulness, robustness, latency, and cost, including red-teaming and safety checks where applicable
• Leverage Amazon Bedrock to prototype and productionize large language model applications, including model selection, prompt templates, routing, and deployment patterns
• Work hands-on with Cortex, including Cortex Analyst, to enable governed analytics experiences and generative artificial intelligence-assisted workflows
• Apply hands-on experience in environments such as Amazon Bedrock, Amazon SageMaker, or Databricks
• Collaborate with engineering teams to deliver scalable services, including application programming interfaces, batch jobs, and pipelines, while ensuring strong software engineering discipline and operational readiness
• Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for large language model applications
• Conduct applied research by studying scientific articles and current techniques in prompting, fine-tuning, evaluation, and agent design, then translating them into practical improvements
• Communicate clearly with technical and non-technical stakeholders by translating business needs into measurable problem statements, solution designs, and success metrics
• Mentor junior data scientists, influence standards, and drive adoption of responsible artificial intelligence practices

Required Qualifications, Capabilities, and Skills

• Advances degree in Data Science, Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience
• 5+ years of applied experience building machine learning or natural language processing solutions, including production deployment in a fast-paced environment
• Experience with natural language processing and large language models, including prompt engineering, retrieval-augmented generation, and evaluation methodologies
• Hands-on experience with Amazon Bedrock or an equivalent managed large language model platform for building and deploying generative artificial intelligence solutions
• Experience with Cortex, including Cortex Analyst or related workflows, in an enterprise setting
• Strong Python skills and familiarity with machine learning or deep learning frameworks such as PyTorch or TensorFlow, and standard machine learning tooling such as pandas, NumNum, and scikit-learn
• Experience building application programming interfaces and integrating large language model or natural language processing solutions into applications and services
• Experience building data pipelines for structured and unstructured data processing, with understanding of embeddings, vector search, indexing, and retrieval patterns
• Experience with software engineering practices, including Git or version control, code quality, testing, and continuous integration and continuous delivery fundamentals
• Strong communication and stakeholder management skills with the ability to present tradeoffs, risks, and results concisely
• Strong analytical skills and working knowledge of financial services, markets, or asset management concepts

Preferred Qualifications, Capabilities, and Skills

• Deep understanding of large language model techniques, including agents, planning, reasoning, and related methods
• Experience with machine learning operations, including experiment tracking, model registry, monitoring, drift and performance tracking, incident management, and rollback
• Experience with cloud deployment patterns, preferably Amazon Web Services, and production runtime environments such as containers or orchestration platforms

To be eligible for this role, you must be authorized to work in the United States. We do not offer any type of employment-based immigration sponsorship for this role. Likewise, JPMorgan Chase & Co. will not provide any assistance or sign any documentation in support of any other form of immigration sponsorship or benefit, including optional practical training (OPT) or curricular practical training (CPT).