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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 a Compliance - Applied AI/ML Lead - Vice President within the Compliance, Conduct Operational Risk Data Analytics organization, you will devise and develop deployable models using AI/ML techniques, algorithms, and statistical methods. You will extract large volumes of structured and unstructured data from multiple sources to build data pipelines and transform information into analysis-ready formats. Your daily work involves formulating methodologies from business problems, designing scalable analytical methods, and preparing technical documentation for model risk governance. You will utilize Python, R, or Scala alongside packages like Pandas, Scikit-Learn, XGBoost, and Torch-Geometric. Key skills include LLM prompt engineering, fine-tuning, and agentic solution development. You will solve complex business problems by applying Graph Neural Networks, clustering, and outlier detection within a highly regulated environment.
Devise and develop Proofs of Concept (POCs) and deployable models using AI/ML techniques.
Extract and transform large volumes of structured and unstructured data from multiple sources into analysis-ready formats.
Develop data pipelines to support the deployment of scalable and effective analytical methods.
Formulate quantitative methodologies and analytical tasks independently from complex business problems.
Design and develop models as part of technology-managed systems or as self-served business applications.
Prepare technical documentation of quantitative models for internal model risk and governance review.
Implement LLM prompt engineering and fine-tuning for specific domains.
Develop agentic solutions to improve consistency and reliability in automated behaviors.
What we're looking for
Bachelor of Science degree in Computer Science, Physical Sciences, Econometrics, Statistics, or another quantitative discipline.
6+ years of related experience in Python, R, or Scala.
Demonstrable theoretical and application knowledge of Machine Learning methods and/or Statistical Models.
Hands-on experience with specific packages including NetworkX, Torch-Geometric, Pandas, Scikit-Learn, XGBoost, and visualization tools like Matplotlib.
Demonstrable experience with LLM prompt engineering, open source LLM fine-tuning, and agentic solution development.
Professional experience in software development for computationally intensive systems and cloud technologies like AWS, GCP, Azure, or Databricks.
Experience in developing and operationalizing data pipelines and assimilating large amounts of data from multiple databases.
Post graduate degrees such as Master’s Degree or PhD (preferred).
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
Machine Learning
Artificial Intelligence
Python
R
Scala
SQL
Spark
SparkML
Hadoop
Hive
AWS
Azure
Google Cloud Platform
NLP
Time Series
Optimization
Operations Research
Statistics