At JPMorgan Chase, AI and technology promote our global operations with unmatched scale and speed. We invest over $18 billion annually in innovation, data leverage, and security to shape the future for our clients, communities, and employees. The Chief Data & Analytics Office (CDAO) accelerates our data and analytics journey, with the Machine Learning Center of Excellence (MLCOE) creating and deploying solutions for complex business challenges. By ensuring data quality and leveraging insights, the CDAO supports our commercial goals, enhancing productivity and risk management through AI and machine learning. 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 Machine Learning Scientist – Natural Language Processing (NLP) - Vice President in the Machine Learning Center of Excellence, you will own the full lifecycle of developing and deploying machine learning solutions, from ideation to production. Acting as a leading voice within JPMC on all things Generative AI (GenAI), you will partner closely with all lines of business to innovate new solutions that drive transformational change for the bank. You will 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. We seek someone who excels in a highly collaborative, fast-paced environment, and holds a strong passion for machine learning to make a significant impact at a leading global financial institution.
Job 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
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline, e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with at least 3 years of experience OR an MS with at least 5 years of industry or research experience in the field
- 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
Preferred qualifications, capabilities, and skills
- Strong background in Mathematics and Statistics; 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
- Published research in areas of Machine Learning or Deep Learning at a major conference or journal
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries.
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