JPMorgan Chase's Chief Data & Analytics Office (CDAO) is looking to accelerate its data and analytics journey by creating and deploying solutions for complex business challenges using AI and machine learning. The Machine Learning Center of Excellence (MLCOE) aims to enhance productivity and risk management through AI and machine learning, develop new products, and improve overall business operations.
Requirements
- Solid background in Generative AI (GenAI) and hands-on experience and solid understanding of machine learning and deep learning methods and 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
- 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
- Familiarity with the financial services industries and continuous integration models and unit test development
- Knowledge in search/ranking or Meta Learning
- Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large-scale distributed environment, and ability to develop and debug production-quality code
Responsibilities
- Research and develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks involving Generative AI (GenAI)
- Act as a thought partner for JPMC leaders and help the business identify and implement new machine learning methods that deliver impact
- Drive cross-functional collaboration with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management to deploy solutions into production
- Lead firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Other
- PhD in a quantitative discipline, e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, OR an MS with at least 3 years of industry or research experience in the field
- Actively participate in our knowledge sharing community, representing your work inside and outside of the firm at leading industry conferences amongst peers and leaders in the space.
- Excels in a highly collaborative, fast-paced environment
- Holds a strong passion for machine learning to make a significant impact at a leading global financial institution.
- Published research in areas of Machine Learning or Deep Learning at a major conference or journal
