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 Summer Associate within the MLCOE, you will apply sophisticated machine learning methods to a wide variety of complex domains within natural language processing, large language models, speech recognition and understanding, reinforcement learning, and recommendation systems. You must excel in working in a highly collaborative environment with their MLCOE mentors, business experts and technologists in order to conduct independent research and deploy solutions into production. You must have a strong passion for machine learning, solid expertise in deep learning with hands-on implementation experience, and invest independent time towards learning, researching, and experimenting with innovations in the field. Learn more about our MLCOE team at jpmorgan.com/mlcoe.

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, an engaging speaker series with our senior leaders and more. Your project will have direct impact on JPMorgan’s businesses, will be integrated into our product pipelines, or be part of published research in top AI/ML conferences. Full-time employment offers may be extended upon successful completion of the program.

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 tasks such as time-series predictions, market modelling, and decision optimizations.
• 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

• Ph.D. or in the last year of a Ph.D. program in machine learning, statistics, mathematics, computer science, economics, finance, science, engineering, or other quantitative fields
• Knowledge of machine learning / data science theory, techniques, and tools
• Scientific thinking, ability to work with literature and the ability to implement complex projects
• Ability to understand business problem, study literature for a solution approach, write high quality code for the chosen method, design training and experimentation to validate the algorithms and implementation, and to 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
• Excellent analytical, quantitative, and problem-solving skills and demonstrated research ability
• Curious, hardworking, detail-oriented and motivated by complex analytical problems

Preferred qualifications, capabilities, and skills

• Knowledge of Financial Mathematics, Stochastic Calculus, Bayesian techniques, Statistics, State-Space models, MCMC, DSGE models, MCTS / distributed compute, NLP, accounting
• Knowledge and experience with Reinforcement Learning methods
• Knowledge of python, Tensorflow, tf-agent, Ray, RLLib, Tune, or other ML frameworks, etc.
• Experience with any of OOP, graph-based computation engines, large scale software development, C++/Java/CUDA, performance focused implementations, numerical algorithms, distributed computing, cloud computing, data transformation pipelines
• Familiarity with continuous integration models and unit test development
• Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal
• Strong passion for machine learning and habits to invest independent time towards learning, researching, and experimenting with innovations across a variety of fields.

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