About the Role
J.P. Morgan AI Research aims to advance cutting-edge AI research and discover principles impactful to J.P. Morgan's clients and businesses. The firmwide Explainable AI Center of Excellence (XAI COE) was recently established, bringing together researchers and practitioners to develop novel techniques, tools, and frameworks for AI/ML model explainability. The XAI COE team consists of XAI experts who conduct primary research to advance the state-of-the-art, publish in top AI/ML venues, partner with internal teams to accelerate AI adoption, and collaborate with leading academic faculty globally on XAI.
The team operates in New York, London, and the Bay Area. Conducting AI research in financial services offers unique opportunities for significant impact within J.P. Morgan and the broader AI community.
Responsibilities
- Work on multiple research projects in collaboration with internal and external researchers and with applied engineering teams.
- Be integral to all aspects of the research lifecycle, including formulating problems, generating hypotheses, developing new algorithms and models, conducting experiments, synthesizing results, gathering data, building prototypes, and communicating research significance.
- Produce outputs such as 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 contributions.
Preferred Qualifications
- PhD in Computer Science (especially AI/ML) or related fields.
- Research publications in prominent AI/ML venues (e.g., conferences, journals).
- Strong expertise, interest, and track record of performing cutting-edge research on Explainable AI (XAI).
- Practical experience with ML platforms such as Tensorflow/Keras, PyTorch.
- Comfort with rapid prototyping and disciplined software development processes.
- Practical software engineering experience in collaborative project settings.
Minimum Requirements
- Masters degree in Computer Science, Statistics, Engineering or related fields.
- Programming skills in Python, Java or C++.
- Proficient understanding of fundamental AI and ML techniques; e.g., A*, regularization.
- Practical experience with statistical data analysis and experimental design.
Professional Skills
- 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 (specific past experience in the domain is not required).