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. 

 

  ABOUT GOLDMAN SACHS
  At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. 
  We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. 
  We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
 
  © The Goldman Sachs Group, Inc., 2023. All rights reserved. Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity

Salary Range 
The expected base salary for this New York, New York, United States-based position is $150000-$225000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

Benefits 
Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non-temporary, full-time and part-time US employees who work at least 20 hours per week, can be found here.