Quantitative Trading and Research - AI Machine Learning Researcher - Associate

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

We are seeking experienced AI Machine Learning (ML) experts to join our team to accelerate our efforts on applying latest AI\ML methods on systematic trading strategy R&D. As part of a small focused team with minimal bureaucracy, you will have great independence to pursue the research directions you think would be most impactful. You are enabled with enough computational resources, and supported by excellent data and engineering teams to realize your vision. Your work will be deployed directly into production trading with P&L responsibility. It will be challenging: this is a field with no easy or obvious solutions.  

As an Associate in the Automated Trading Strategies (ATS) group, you are responsible for systematic trading across FX, Rates, Commodities, and Credit markets. You will focus on enhancing every aspect of our models, including data featurization, architecture design, training dynamics, and trading decision processes. You will be part of a rapidly expanding team, gaining a comprehensive understanding of market making and exchange trading, encompassing alpha generation, portfolio construction/optimization, and trade execution algorithms.

Job Responsibilities

• Apply AI\ML model to find predictive pattern from large dataset 
• Integrate prediction model into existing strategies or setup new one
• Contribute to team’s AI\ML libraries as well as production algo system

Required qualifications, capabilities, and skills

• Advanced degree (Masters, PhD, or equivalent) in Machine learning, Mathematics & Statistics, Computer Sciences, Engineering or related fields. 
• At least 1 year experience in machine learning and deep learning research for any domain.
• Relevant experience using frameworks such as PyTorch, TensorFlow and equivalent programs. 
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward.

Preferred qualifications, capabilities, and skills:

• Strong engineering skills valued, efficiency in other languages (Java/C++/C#) beyond Python is a plus. 
• Finance experience is not required.