Are you passionate about quantitative trading? Join a global team where your research and development skills will directly impact our systematic trading business. At JPMorganChase, you’ll collaborate with talented professionals, tackle complex challenges, and help shape the future of automated trading. This is your opportunity to make a meaningful contribution in a dynamic, fast-paced environment. Discover how you can grow your career and make a difference.

As Associate in Automated Trading Strategies (ATS), you will be part of a global team of quantitative traders responsible for designing, implementing, and running automated systematic trading strategies across asset classes. You will focus on the full stack of running a systematic trading book from alpha generation to execution and risk-management. In this role, you will use your research and development skills to implement strategies in production code and contribute to the day-to-day running of the business.

 

Job Responsibilities

Produce innovative research on quantitative trading strategies Build end-to-end machine learning pipelines from data ingestion, model design and training, to production deployment for real-time inference Implement production-grade code for a fully automated strategies Monitor performance and resolve day-to-day trading issues Collaborate with other teams to identify opportunities for revenue growth

Required Qualifications, Capabilities, and Skills

Advanced degree (PhD preferred) in computer science, mathematics, physics or a related quantitative field with research or industry experience in machine learning/deep learning.  Strong programming skills in Python for ML research and production (e.g., PyTorch), plus proficiency in one compiled language (C++ or Java). Capable of independent research and idea generation Excellent written and verbal communication skills Active interest in markets and quantitative trading

Preferred Qualifications, Capabilities, and Skills

Experience in sequential modelling and ability to quickly reproduce state-of-the-art results Having published relevant papers in top international conferences or journals (e.g. NIPS, ICML) Experience with large-scale data sets and optimizing low-latency systems