The GT Chief Data & Analytics Office (CDAO) at JPMorgan Chase develops data science solutions for a wide range of global technology (GT) use cases. Data scientists within GT CDAO tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for data, cyber, software, and technology infrastructure.
As the Applied AI ML Lead-Vice President in the GT Chief Data & Analytics Office(CDAO) at JPMorgan Chase, you will apply sophisticated machine learning, LLM, and agentic methods to a wide variety of complex tasks, large data sets, data lakes, and MCP services, to deliver solutions including agentic assistants, anomaly detection, classification, and document corpus retrieval.
You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you. We value solid expertise in Deep Learning with hands-on implementation experience, strong analytical thinking, continuous improvement, and high motivation.
Job Responsibilities
- Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems technology and infrastructure domains
- Collaborate with partner teams in strategy, governance, controls, and engineering to deploy solutions into production
- Bridge advanced AI research with robust engineering to build innovative, production-ready solutions.
Required qualifications, capabilities and skills
- PhD in a quantitative discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.) with 2 years experience Or Masters with 5 years of industry or research experience in the field.
- Hands-on experience and solid understanding of machine learning, LLMs fine-tuning including PEFT, and agentic design
- Experience with advanced agentic workflow orchestration, including multi-agent coordination, stateful task management, and integration with enterprise event-driven architectures.
- Extensive experience with machine learning and deep learning toolkits (e.g.: PyTorch, Transformers, NumPy, Scikit-Learn, Pandas)
- Extensive experience with multi-modal large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, vector databases, opensource models, RAG, agentic systems & workflows, LLM fine-tuning)
- Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
- Experience with big data and scalable model training
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking, and detail-oriented, and motivated by complex analytical problems
- Ability to work both independently and in highly collaborative team environments
**Preferred qualifications, capabilities, and skills **
- Experience with developing and deploying machine learning models for enterprise-scale meta data management, data governance, data quality on cloud data lakes, cyber threat detection
- Experience with AWS deployment
- Ability to develop and debug production-quality code and leverage AI pair programming
- Strong background in Mathematics and Statistics