AI Research Scientist - Senior Associate - AI

Overview

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.

• Work on multiple research projects in collaboration with internal and external researchers and applied engineering teams
• Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments, synthesize results, gather data, build prototypes, and communicate research significance
• Contribute to publications in AI/ML conferences and journals, high-impact business applications, open-source software, and patents
• Participate in relevant top-tier academic conferences, organize workshops, and engage with the AI research community to broaden the impact of your contributions

• PhD in Computer Science, Statistics, Engineering, or related fields
• Programming skills in Python
• Proficient understanding of fundamental AI and ML techniques (e.g., A*, regularization)
• Practical experience with statistical data analysis and experimental design
• Curiosity, creativity, resourcefulness, and a collaborative spirit
• Effective verbal and written communication skills with technical and business audiences
• Demonstrated ability to work on multi-disciplinary teams with diverse backgrounds
• Interest in problems related to the financial services domain

• Research publications in prominent AI/ML or Software Engineering venues (e.g., conferences, journals)
• Strong expertise in Trustworthy AI topics like explainability, safety, robustness, fairness
• Hands-on experience in developing and using multimodal foundation models
• Practical experience with ML platforms such as Tensor Flow/Keras, Py Torch
• Comfort with rapid prototyping and disciplined software development processes
• Practical software engineering experience in collaborative project settings