The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

As a Machine Learning Scientist - Quant AI - Vice President within the Machine Learning Center of Excellence, you will have the opportunity to apply sophisticated machine learning methods to a wide variety of complex tasks. You will collaborate with all of JPMorgan's lines of business and functions to deliver software solutions with high technology and business impact. You will also research, develop, and productionize high-performance machine learning models, quantitative models, and applications. This role requires a strong passion for machine learning, solid expertise in Deep Learning, and a deep desire to learn and grow.

Job Responsibilities

Research, develop and productionize high performance machine learning models, quantitative models, and applications. Collaborate with all of JPMorgan's lines of business and functions to deliver software solutions, making high technology and business impact. Design and implement highly scalable and reliable data processing pipelines and perform analysis and insights to drive and optimize business result.

 Required qualifications, capabilities, and skills

PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with three years of industry experience Or an MS with at least five years of industry or research experience in the field. Solid programming skills with C/C++, Java, Python or other equivalent languages. 2 years of experience with software development in C++ or more programming languages. 2 years of experience with data structures and algorithms. Deep knowledge in Machine Learning, Deep Learning, Data Mining, Information Retrieval, Statistics. Experienced in one or more major machine learning frameworks: Tensorflow, Pytorch, JAX, Keras, MXNet, Scikit-Learn. Experience in ETL pipelines, both batch and real-time data processing. Strong analytical and critical thinking skills. Self-motivation, great communication skills and team player.

Preferred qualifications, capabilities, and skills

Experience in computational graphs and just in time (JIT) compilation. Experience in Generative AI, LLMs and AI Agents is a plus. Knowledge in hardware accelerators / GPUs, manycore architecture and profiling tools. Knowledge in Deep Learning frameworks backend. Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes. Experience in distributed system design and development Previous experience with derivatives modelling or portfolio management is preferred