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$148,525–$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
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Vice President – Data Scientist Lead (LLM/GenAI) within the Global Services Insights & Analytics team, you will lead data-driven initiatives to improve operational performance and controls for Commercial Banking. You will design and deliver large language model solutions for content extraction, enterprise search, reasoning, and summarization while ensuring reliable, governed capabilities. 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 Amazon Bedrock, Cortex, SageMaker, or Databricks to prototype and productionize applications. Technical requirements include Python, PyTorch, TensorFlow, pandas, NumPy, and scikit-learn. You will build data pipelines for structured and unstructured data while collaborating with engineering teams to deliver scalable APIs and batch jobs within the financial services domain.
Develop and deliver LLM solutions for content extraction, semantic search, summarization, and recommendation use cases.
Design and manage prompt-based systems and retrieval-augmented generation (RAG) with agentic workflows.
Build evaluation frameworks to measure accuracy, faithfulness, robustness, latency, and cost for AI models.
Prototype and productionize generative AI applications using Amazon Bedrock and Cortex platforms.
Build data pipelines for structured and unstructured data to enable indexing and retrieval for LLM applications.
Conduct applied research on prompting, fine-tuning, and agent design to improve practical AI solutions.
Collaborate with engineering teams to deliver scalable APIs, batch jobs, and production-grade machine learning operations.
Translate complex business needs into measurable problem statements and solution designs for technical and non-technical stakeholders.
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 for production deployment.
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.
Experience with Cortex, including Cortex Analyst or related workflows, in an enterprise setting.
Strong Python skills and familiarity with frameworks like PyTorch or TensorFlow, plus tools like pandas, NumPy, and scikit-learn.
Experience building data pipelines for structured/unstructured data and integrating LLM solutions into applications via APIs.
Machine Learning
Deep Learning
Generative AI
LLMs
NLP
Python
Java
C++
Transformers
Hugging Face
TensorFlow
PyTorch
NumPy
Scikit-Learn
Pandas
AWS
SageMaker
Prompt Engineering
Big Data
GenAI
LLM
Python
Spark
AWS
SQL
TensorFlow
PyTorch
Scikit-learn
OpenAI API
RAG
Agentic AI
Transformer
NLP
Data Wrangling
Time Series
Deep Learning
Reinforcement Learning
Java
R
LLM
NLP
Machine Learning
Python
Spark
Scala
Java
Data Structures
Algorithms
Search
Big Data
Classification
Regression
Recommender Systems
Databases
System Development