Data Scientist Lead - Applied AI ML

As a Lead Data Scientist with the Chief Data Office, you'll help shape the future of the Chief Administrative Office and its businesses by applying world-class machine learning expertise. You'll collaborate on a wide array of product and business problems with cross-functional partners across Finance, Supplier Services, Data Security, Global Real Estate, and Customer Experience. You'll use data and analysis to identify and solve our division's biggest challenges and develop state-of-the-art GenAI and LLM models to solve real-world problems. By joining JP Morgan Chief Data Office (CAO), you'll be part of a world-class data science community dedicated to problem solving and career growth in ML/AI and beyond.

Job Summary

• Lead hands-on development and technical direction for GenAI, LLM, and agentic AI solutions.
• Collaborate with cross-functional teams to deliver scalable, production-ready AI systems.
• Drive adoption of modern ML infrastructure and best practices.
• Communicate technical concepts and results to both technical and business stakeholders.
• Ensure responsible AI practices and model governance.

Job Responsibilities

• Master's or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
• 10+ years of hands-on experience in applied machine learning, including GenAI, LLMs, or foundation models.
• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, JAX).
• Proven experience designing, training, and deploying large-scale ML/AI models in production environments.
• Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks.
• Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm).
• Solid grasp of MLOps tools and practices (MLflow, model monitoring, CI/CD for ML).
• Ability to communicate complex technical concepts to both technical and business stakeholders

Preferred Qualifications

• Experience with high-performance computing and GPU infrastructure (NVIDIA DCGM, Triton Inference).
• Familiarity with big data processing tools and cloud data services.
• Background in financial services or regulated industries.
• Published research or contributions to open-source GenAI/LLM projects.