About the job
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. As an AI Research Senior Associate in J.P. Morgan AI Research, you will work on novel techniques, tools, and frameworks to model and solve complex large-scale problems, collaborating with experts in various technical and business disciplines, contributing to high-impact business applications at the cutting edge of AI.
Responsibilities
Work on multiple commerically-orientated research projects in collaboration with internal data scientists, applied engineering teams and business stakeholders
Formulate problems, generate hypotheses, develop new algorithms and models, conduct experiments, synthesize results, gather data, build innovative solutions, and communicate research significance
Contribute to high-impact business applications, open-source software, and patents
Develop state-of-the art machine learning models to solve real-world problems at scale
Qualifications
Minimum
PhD in Computer Science, Engineering, or related fields with two years of industry experience
Programming skills in Python
Proficient understanding of fundamental AI and ML techniques
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
Preferred
Research publications in prominent AI/ML, Software Engineering venues (e.g., conferences, journals)
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