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This listing doesn't post a salary. Most similar roles pay
$158,212–$214,500.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
JOB TITLE: Vice President – Data Scientist Lead (LLM/GenAI)
As a Vice President – Data Scientist Lead in the Global Services Insights & Analytics team, you will lead data-driven initiatives to improve operational performance, efficiency, service, and controls within Commercial Banking. You will design and deliver large language model-powered solutions for content extraction, enterprise search, question answering, reasoning, summarization, and recommendations. You will partner with engineering and product teams to deploy reliable, scalable, and governed generative artificial intelligence capabilities using Amazon Bedrock and Cortex platforms. Your daily work involves building prompt-based and retrieval-augmented generation systems, managing agentic workflows, and developing evaluation frameworks for accuracy and safety. You will utilize Python, PyTorch, TensorFlow, pandas, NumPy, and scikit-learn to build data pipelines and production-grade machine learning operations for financial services use cases.
Develop and deliver generative AI and LLM solutions for content extraction, semantic search, and summarization.
Design and manage prompt-based and retrieval-augmented generation systems with agentic workflows and multi-step reasoning.
Build evaluation frameworks to measure accuracy, faithfulness, robustness, latency, and cost for production models.
Prototype and productionize LLM applications using Amazon Bedrock, including model selection and deployment patterns.
Build and maintain data pipelines for structured and unstructured data to enable retrieval and indexing.
Collaborate with engineering teams to deliver scalable APIs, batch jobs, and pipelines with strong software engineering discipline.
Conduct applied research on prompting, fine-tuning, and agent design to translate scientific techniques into practical improvements.
Mentor junior data scientists and drive the adoption of responsible artificial intelligence practices.
What we're looking for
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 frameworks such as PyTorch or TensorFlow, and tools like pandas, NumPy, and scikit-learn.
Experience building data pipelines for structured and unstructured data, including embeddings, vector search, indexing, and retrieval patterns.
Machine Learning
Deep Learning
Generative AI
LLMs
NLP
Python
Java
C++
PyTorch
TensorFlow
Hugging Face
Transformers
NumPy
Scikit-Learn
Pandas
AWS
SageMaker
EC2
Glue
Prompt Engineering
Big Data
GenAI
LLM
Python
SQL
AWS
Spark
TensorFlow
PyTorch
Scikit-learn
R
Java
RAG
Agentic AI
Transformer
OpenAI API
NLP
Deep Learning
Reinforcement Learning
Time Series
GenAI
LLM
Python
Spark
AWS
SQL
TensorFlow
PyTorch
Scikit-learn
OpenAI API
R
Java
RAG
Agentic AI
Transformer
Deep Learning
NLP
Statistics
Data Wrangling
Generative AI
Agentic AI
Data Architecture
Lakehouse Architecture
Data Governance
Platform Engineering
AI Platform
Metadata Management
Data Quality
Mainframe
Identity Resolution
Enterprise Architecture
Data Products
AI Strategy