J.P. Morgan Compliance, Conduct, and Operational Risk (CCOR) Data Science function is at the forefront of developing innovative AI and ML solutions to enhance risk management and compliance processes. Our team leverages advanced analytics, AI, Machine learning technologies, including Large Language Models (LLMs), to transform how we manage compliance, conduct, and operational risks across the organization.

Join our CCOR Data Science, Applied AI & ML team, where you will play a crucial role in developing AI/ML solutions to enhance compliance and risk management. Your primary responsibilities will include leveraging advance AI / ML techniques, LLMs and Agentic AI to develop and deploy models for various tasks such as anomaly detection, risk assessment, and compliance monitoring. You will work with diverse datasets to improve our risk management processes and ensure regulatory compliance. As part of a leading analytics community, you will have opportunities for skill development and career growth in AI, machine learning, and data science.

Job responsibilities:

Design, deploy, and manage AI/ML models, leverages advance AI/ML techniques, LLMs and Agentic AI for developing compliance and risk management related solution. Conduct research on AI techniques to enhance model performance in the compliance and risk domains. Collaborate with cross-functional teams to identify requirements, develop solutions, and deploy models in the production system. Communicate technical concepts effectively to both technical and non-technical stakeholders. Develop and maintain tools and frameworks for model training, evaluation, and optimization. Analyze and interpret data to evaluate model performance and identify areas for improvement.

Required qualifications, capabilities, and skills:

Master’s degree in quantitative discipline or an MBA with an undergraduate degree in areas such as Computer Science, Statistics, Economics, Mathematics etc. from top tier Universities.  Minimum of 3 years of relevant experience in developing AI/ML solutions, with recent exposure to LLMs and Agentic AI. Programming skills in Python, with experience in frameworks like PyTorch or TensorFlow. Thorough knowledge of machine learning concepts, including transformers and language modeling. Experience in data pre-processing, feature engineering, and data analysis. Excellent problem-solving skills and the ability to communicate ideas and results clearly. Familiarity with data structures and algorithms for effective problem-solving in machine learning workflows.