Machine Learning Scientist - Time Series and Reinforcement Learning - Machine Learning Center of Exc

JPMorgan Chase - Seattle, WA

Full-time - Senior

Seattle, WA

Credit Intermediation and Related Activities

About the position

The Machine Learning Scientist position at JPMorgan Chase focuses on applying advanced machine learning methods, particularly in time series and reinforcement learning, to optimize business decisions across various banking applications. This role involves developing scalable tools and collaborating with different lines of business within the firm to create production-level machine learning applications that address real-world financial problems.

Responsibilities

• Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems for various issues related to finance, economics, and operations of JP Morgan.
• Collaborate with all of JPMorgan Chase's lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.
• Lead your own project, suggesting, collecting, and synthesizing requirements.
• Create an effective roadmap towards the deployment of a production-level machine learning application.

Requirements

• PhD in a quantitative discipline, e.g., Computer Science, Mathematics, Operations Research, Data Science.
• For VP position, minimum 3 years of working experience.
• Experience in machine learning project development.
• Knowledge of machine learning/data science theory, techniques, and tools.
• Ability to understand business problems, study literature for solution approaches, write high-quality code, design training and experimentation, and evaluate model performance metrics aligned with business goals.
• Solid written and spoken communication skills to effectively communicate technical concepts and results to both technical and business audiences.
• Curious, hardworking, detail-oriented, and motivated by complex analytical problems.

Nice-to-haves

• Solid time series analysis, speech recognition, NLP, or financial engineering background.
• Strong background in Mathematics and Statistics.
• Published research in relevant fields.