Quantitative Research Group Job at Quantitative Brokers

Quantitative Brokers New York, NY

Senior Machine Learning Intern - Quantitative Research Group

About the Firm:

Quantitative Brokers (QB) is a rapidly expanding fin-tech firm specializing in agency algorithms in futures and fixed income markets. QB has offices in New York, London, Sydney, and Chennai and is led by the co-founders Robert Almgren and Christian Hauff. Additionally, Deutsche Börse AG, a well-known German conglomerate, owns a majority ownership interest in QB. Based on tested quantitative research, QB’s culture is founded on curiosity, creativity, and hard work. Our team is intelligent, innovative, and constantly hunting for inspired solutions to reduce implicit trading costs and manage execution risk for our clients.

Quantitative Research Group:

The Quantitative Research group in QB predominantly comprises professionals with Ph.D. in physics, engineering, financial economics, or at least a graduate degree in these areas. Our current and past interns have been doctoral and master’s level students from some of the ivy league universities of the world.

As a member of our Quantitative Research team, you will focus on improving the performance of our client-focused trade execution algorithms. You will work closely with the head of the research, the rest of the quant team, and Robert Almgren, the Chief Data Scientist of QB. As a critical member of this team, you will be responsible for building, enhancing, and maintaining our signal framework and enhancing our futures and options on futures algorithms.


Skills and Background

Primary Responsibilities:

  • Machine learning-based research to improve execution algorithms

  • Develop and implement price forecast signals

Skills and Experience:

  • Pursuing an advanced degree (MS or Ph.D.) in a quantitative field

  • Experience and/or interest in high-frequency trading and market microstructure

  • Programming experience in R/Python to develop production-quality systems

  • Expertise in using machine learning techniques such as variational auto-encoders, neural networks, and reinforcement learning.

NYCJulie KangAugust 27, 2021
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