The role
From JPMorgan Chase's own posting.
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. As a part of CDAO, The Machine Learning Center of Excellence (MLCOE) partners across the firm to shape, create, and deploy Machine Learning Solutions for our most challenging business problems. 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 Time Series and Reinforcement Learning Summer Associate within the MLCOE, you will be part of a group of technologists, ML researchers and engineers who are passionate about machine learning and scale. This team works closely in exploring cutting-edge research and applying the latest Machine Learning techniques to J.P. Morgan’s unique data assets, collaborating directly with traders, portfolio managers, quants, data scientists and salespeople to drive the data-led transformation of our businesses.
Our Summer Associate Internship Program begins in June, depending on your academic calendar. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, engaging speaker series with senior leaders and more. Full-time employment offers may be extended upon successful completion of the program. Learn more about our MLCOE team at jpmorgan.com/mlcoe .
Job responsibilities
Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
Develop state-of-the art machine learning models to solve real-world problems and apply it to different tasks
Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Required qualifications, capabilities, and skills
Enrolled in a PhD or MS program in Econometrics, Finance/Accounting, Mathematics, Computer Science, Operations Research or related quantitative field with an expected graduation date of December 2027 through August 2028
Solid programming skills with C/C++, Python, JAVA or other equivalent languages
Deep knowledge in Machine Learning, Data Mining, Applied Mathematics, Optimization, and Statistics
Knowledge with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, JAX) and ETL pipelines, both batch and real-time data processing
Scientific thinking, ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
Solid written and spoken communication to effectively communicate technical concepts and results to both technical, and business audiences
Ability to work both independently and in highly collaborative team environments
Preferred qualifications, capabilities, and skills
Interest in quantitative finance and/or portfolio management
Knowledge with CPU, GPU architecture, high-performance computing and XLA compilers
Experience in distributed system design and development
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