What are the responsibilities and job description for the Quantitative Research - Trading Services - Associate position at JPMorgan Chase?
Quantitative Research (QR) is an expert group at J.P. Morgan specializing in stochastic and statistical modeling, data analytics, optimization, and other quantitative methods. In Securities Services, QR applies cutting-edge AI/ML techniques to fundamentally transform the way we do business.
Job Summary
As an Analyst or Associate in the Quantitative Research (QR) Trading Services team, you will support Agency Securities Finance Trading in tackling its most technically complex business problems. This could range from developing ML applications that make market-leading predictions, to leveraging LLMs, to handling vast data sets using J.P. Morgan's cloud capabilities. You will work in close partnership with the business to identify their most pressing pain points and iterate towards solutions that really work for them. Candidates with a strong quantitative background and a passion for solving real-world problems are encouraged to apply.
Job responsibilities
- Work with traders to develop AI/ML-driven analytics and automation that support their business goals
- Carry out model development from research and iterative testing through production deployment and ongoing support
- Perform large-scale analysis on our proprietary dataset
- Make business-focused, commercial recommendations through effective presentations to various stakeholders
- Partner with colleagues in Quantitative Research, Technology, and the Chief Data and Analytics Office (CDAO) to drive the Securities Services data and analytics strategy forward
Required qualifications, capabilities, and skills
- Advanced degree (PhD or MS) or equivalent in a quantitative field: Statistics, Mathematics, Physics, Computer Science, Engineering, etc.
- Candidates will be considered for an Analyst or Associate position depending on credentials and industry experience.
- Robust understanding of Machine Learning, Statistics, and Mathematics, both in fundamentals and in application.
- Excellent coding skills in Python and solid software engineering fundamentals; fluent command of NumPy, Pandas, Matplotlib, and scikit-learn.
- Proven track record of developing solutions to real-world data science problems, end-to-end from prototype to production.
- Effective verbal and written communication skills, and the ability to present technical information to a non-technical audience.
- Passion for learning, sharing knowledge, building collaborations, and getting things done.
Preferred qualifications, capabilities, and skills
- Candidates with experience in finance or working with financial data sets are preferred.