Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office.
As a Data Scientist Senior Associate in the Chief Data & Analytics Office, you will lead the development and deployment of innovative AI and machine learning solutions. You will collaborate with cross-functional teams to address complex business challenges, drive adoption of modern ML practices, and ensure responsible AI governance. You will have the opportunity to work with state-of-the-art technologies and contribute to a culture of technical excellence and continuous learning.
Job responsibilities:
- Lead the hands-on design, development, and deployment of advanced AI, GenAI, and large language model solutions.
- Serve as a subject matter expert on a wide range of machine learning techniques and optimizations.
- Collaborate with product, engineering, and business teams to deliver scalable, production-ready AI systems.
- Conduct experiments using the latest ML technologies, analyze results, and tune models for optimal performance.
- Own end-to-end code development in Python for both proof-of-concept and production-ready solutions.
- Integrate generative AI within the ML platform using state-of-the-art techniques.
- Drive adoption of modern ML infrastructure, tools, and best practices.
- Optimize system accuracy and performance by identifying and resolving inefficiencies.
- Communicate technical concepts and results to both technical and business stakeholders.
- Ensure responsible AI practices, model governance, and compliance with regulatory standards.
- Mentor and guide other AI engineers and scientists, fostering a culture of continuous learning.
Required qualifications, capabilities, and skills:
- Master’s or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
- Minimum 5 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models.
- Experience programming in Python; experience with ML frameworks such as PyTorch or TensorFlow.
- 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).
- Strong communication skills with the ability to explain complex technical concepts to diverse audiences.
- Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
- Experience applying data science and ML techniques to solve business problems and passion for detail, follow-through, and technical excellence.
Preferred qualifications, capabilities, and skills:
- Experience with high-performance computing and GPU infrastructure (e.g., NVIDIA DCGM, Triton Inference).
- Familiarity with big data processing tools and cloud data services.
- Advanced knowledge in reinforcement learning, meta learning, or related advanced ML areas.
- Experience with search/ranking, recommender systems, or graph techniques.
- Background in financial services or regulated industries.
- Experience with building and deploying ML models on cloud platforms such as AWS Sagemaker, EKS, etc.
- Published research or contributions to open-source GenAI/LLM projects.