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$133,328–$202,289.
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 Data Scientist Lead within the Commercial & Investment Bank with the Healthcare Provider team, you will lead a team of data scientists to build advanced solutions for image classification, text categorization, and intelligent data extraction from scanned documents. You will manage the full machine learning lifecycle, from prototyping to production deployment on AWS EKS, while defining analytical strategies for document understanding using computer vision and NLP. Your daily work involves building and fine-tuning multimodal models to extract structured fields from complex documents. You will utilize Python, PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, OpenCV, pandas, NumPy, and SQL. Additionally, you will leverage AWS SageMaker, Amazon Bedrock, and OCR technologies to process TIF/PNG files. The role focuses on solving document processing challenges by integrating textual content, spatial layout, and visual features into automated workflows.
Lead and mentor a team of data scientists in developing advanced analytics and modeling projects.
Define the analytical strategy for document understanding using computer vision, NLP, and multimodal approaches.
Build and fine-tune multimodal models to extract structured fields and key-value pairs from complex scanned documents.
Design rigorous experimentation frameworks including A/B testing, cross-validation, and statistical significance testing.
Establish best practices for annotation quality management, training data curation, and active learning strategies.
Manage the full machine learning lifecycle from prototyping to production deployment on AWS SageMaker and EKS.
Develop and optimize workflows for preparing large datasets for deep learning model training.
Integrate analytical models into production document processing pipelines in collaboration with engineering teams.
What we're looking for
Bachelor's degree, MS, or PhD in a quantitative discipline such as Computer Science, Mathematics, Operations Research, or Data Science.
7+ years of experience in data science or quantitative analytics.
At least 2+ years of experience in document AI, computer vision, or NLP domains.
Advanced proficiency in Python and deep experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, OpenCV, pandas, NumPy, matplotlib, and seaborn.
Hands-on experience with CNN/transformer architectures, multimodal document understanding, and OCR technologies for text extraction from scanned documents.
Experience with AWS SageMaker, Amazon Bedrock, and containerized deployments on AWS EKS for productionizing models at scale.
Proficiency in SQL with knowledge of Oracle databases, and working knowledge of Java and Groovy for engineering collaboration.
Domain expertise in the healthcare industry (preferred).
Python
R
SQL
Stata
Apache Spark
Apache Airflow
AWS
Lambda
EC2
ECS
IAM
Athena
S3
Ansible
Terraform
Linux
Git
GitLab
Machine Learning
RAG
Statistical Modeling
Big Data