Applied AI ML Senior Associate - Machine Learning Center of Excellence - Time Series Reinforcement Learning
Role details
Job location
Tech stack
Job description
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., * 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 analysis and modelling, constrained optimization and prediction for large systems, prescriptive analytics, and decision-making in dynamical systems
- Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
- Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Requirements
As an Applied AI ML Senior Associate in Machine Learning Center of Excellence, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including time series analysis, reinforcement learning, causal inference, and natural language processing. You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you. We value solid expertise in Machine Learning and Econometrics with hands-on implementation experience, strong analytical thinking, a deep desire to learn and high motivation., * PhD in a quantitative discipline, e.g. Econometrics, Finance/Accounting, Mathematics, Computer Science, Operations Research
- Ability to conduct literature research in unfamiliar fields
- Hands-on experience and solid understanding of machine learning and deep learning methods
- Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
- Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
- Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
- Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems, * Strong background in Mathematics and Statistics and familiarity with the financial services industries;
- Solid knowledge in financial reports analysis; understand relationships among items in Balance Sheet, Income Statement, and Cashflow statement
- Ability to develop and debug production-quality code and solid experience in writing unit tests, integration tests, and regression tests;
- Published research in areas of Machine Learning/Deep Learning/Reinforcement Learning OR Finance/Accounting at a major conference or journal
