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.

 This role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates. 

Machine Learning Scientist – Quant AI - Vice President

The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems.

The candidate must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

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