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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.
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As an Applied AIML Data Scientist Lead - Vice President on the Legal Applied AI/ML team in Corporate Technology, you will address complex business problems including semantic search, question answering, and document analysis. You will develop, test, and evaluate AI/ML models by leveraging a rich pool of internal and external data to automate service inquiries. Your daily work involves building GenAI and LLM solutions, implementing optimization strategies for fine-tuning generative models, and executing full model development lifecycles including data wrangling and training. You will utilize Python, Spark, SQL, and frameworks like TensorFlow, PyTorch, Scikit-learn, and the OpenAI API. Additionally, you will implement RAG and Agentic AI frameworks while utilizing advanced prompting strategies like Chain-of-Thoughts to solve technical challenges and present meaningful insights to both technical and non-technical audiences.
Develop GenAI and LLM solutions to solve complex business problems.
Implement optimization strategies to fine-tune generative models for specific use cases.
Execute the full model development lifecycle including data wrangling, training, testing, and selection.
Translate technical results into meaningful insights for both technical and non-technical audiences.
Stay current on AI research to implement cutting-edge techniques and leverage external APIs.
Design and implement pipelines using RAG and Agentic AI frameworks.
Integrate user feedback to create agentic refinement and self-improving AI applications.
What we're looking for
PhD in Computer Science or a related quantitative discipline with 2+ years of experience, or a BS/MS in a related field with 4+ years of experience.
Practical expertise with LLM projects and other supervised/unsupervised techniques with a track record of deploying models in production.
Proficiency in Python and SQL, along with practical experience in languages like R or Java.
Experience working with large, complex datasets and using ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
Solid understanding of statistics, machine learning fundamentals (classification, regression, time series, deep learning, reinforcement learning), and Transformer architectures.
Ability to identify AI/LLM challenges, implement optimizations, and tune models for NLP applications.
Experience working with engineering teams to operationalize ML models (preferred).
Expertise in RAG, Agentic AI frameworks, and advanced prompting strategies like Chain-of-Thoughts (preferred).
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
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
Machine Learning
Artificial Intelligence
Python
R
Scala
Hadoop
Spark
SparkML
SQL
Hive
AWS
Azure
Google Cloud Platform
Natural Language Processing
Time Series
Optimization
Operations Research
Statistics