Strat - Futures Strats Team GBM Public 

Futures Strats are responsible for all aspects of the futures electronic trading business, providing sophisticated execution-related services to the firm’s clients, with a particular focus on automated execution algorithms. They are responsible for research, design, implementation, testing and support of high-performance algorithmic trading systems and strategies for the firm’s futures trading businesses. The team interfaces on a regular basis with clients, sales-trading, technology, and other Strats teams.

Responsibilities: 

 

• Design, build and maintain complex, scalable, low latency and high-capacity quantitative models for real time algorithmic trading, order state management, risk management, and other execution functions.
• Design and implement novel trading algorithms and approaches to provide generalizable solutions to complex, high-dimensional problems, ensuring efficiency and scalability across different markets. 
• Build state of the art execution and market making algos using statistical and mathematical approaches and develop new models to leverage trading capabilities. 
• Work with super-large datasets to extract data and turn data into tradable information. 
• Provide quantitative analysis and analyze noisy data. Generate ideas to build complex signals and design overall strategies. Combine methods of theoretical physics and artificial intelligence to generate predictive mathematical models. 
• Engineer software applications for high frequency trading and develop logical theories for trade execution.
• Develop and implement feedback mechanisms to continuously improve the accuracy and effectiveness of the models.  
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner. 
• Collaborate with cross-functional teams to understand business requirements and translate them into actionable solutions. 
  
   

Requirements: 

• A bachelor’s degree in Computer Science, Operations Research, Math, Physics or Statistics. 
• Proficiency in programming languages like Python, Java or C++ and the ability to write efficient, clean, and maintainable code. 
• Background in Probability, Statistics, Machine Learning, Natural Language Processing, Reinforcement Learning, Large Language Models is desirable.

• Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.