2026 Machine Learning Center of Excellence Summer Associate – Time Series & Reinforcement Learning Internship – (6 months)

Machine Learning Center of Excellence (MLCOE) Summer Associate (2026)

The Machine Learning Center of Excellence (MLCOE) is a machine learning team that continually advances state-of-the-art methods to solve real-world financial problems using the company’s unique datasets.

As a 2026 MLCOE Summer Associate, you will apply advanced machine learning techniques across domains including finance, banking, economics, marketing, natural language processing, reinforcement learning, accounting, and operations management. You will have the opportunity to lead groundbreaking machine learning initiatives leveraging unique datasets to solve complex financial problems.

Job Responsibilities

• Create strategically important AI/ML applications in the Chief Data & Analytics office. Work spans multiple lines of business, including Commercial & Investment Bank, Asset & Wealth Management, Consumer & Community Banking, and across the organization—from front office sales and trading to operations, technology, and finance.
• Explore novel and complex challenges that may transform how the firm operates.
• Collaborate closely with MLCOE mentors, business professionals, and technologists by conducting independent research and delivering solutions for the business.
• Demonstrate strong passion for machine learning, robust deep learning expertise with practical implementation experience, and a dedication to learning, researching, and experimenting with innovation in the field.

Required Qualifications, Capabilities, and Skills

• Enrolled in or completed a PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields), or equivalent research/industry experience. Post-doctoral candidates and career changers are welcome.
• Expected PhD graduation between December 2026 and August 2027, or already graduated.
• Strong background in programming, mathematics, and statistics, including areas such as stochastic calculus, Bayesian techniques, state-space models, MCMC, and MCTS.
• Published research in machine learning, science, engineering, quantitative psychology, business, or related fields.
• Knowledge and experience in machine learning and reinforcement learning, along with familiarity with state-of-the-art practices and domains such as finance, economics, accounting, marketing, or operations research.
• Proficiency in Python and experience with machine learning/deep learning frameworks such as TensorFlow, TensorFlow Probability, and JAX.
• Scientific mindset with the ability to design experiments and training frameworks, and define and evaluate model performance metrics aligned to business objectives.
• Ability to develop, debug, and maintain production-quality code, including familiarity with continuous integration and unit testing.
• Strong written and verbal communication skills, with the ability to explain technical concepts and results to both technical and business audiences.
• Innovative, curious, hardworking, detail-oriented, and motivated by solving complex analytical problems; able to work independently and in highly collaborative team environments.
• Familiarity with the financial services industry and relevant certifications such as CFA, ACCA, PCAP, or other finance, accounting, programming, software engineering, Coursera, or equivalent professional qualifications.