Vice President – Data Scientist Lead (LLM/GenAI)

JPMorgan Chase & Co.•Chicago, IL

1d•$128,250 - $200,000

About The Position

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.

Requirements

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, NumPy, 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

Nice To Haves

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

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