As a Quantitative Researcher Intern, your focus is on identifying trading opportunities, but you can add even more value with strong quantitative skills and some coding proficiency to accelerate the innovation process and help others leverage your work.
While interest in trading is key, a background in finance is definitely not. Our team is built mostly from academia — professors, postdocs, PhDs, and undergraduates — not from other trading firms. We seek mental diversity and add a select group of academics each year from a wide range of disciplines.
We’re looking for highly analytical people (math, physics, computer science, statistics, electrical engineering, etc.) who want to help build the research-driven trading firm of the future. To do that, you’ll need the following qualities:
Persistent Drive to Improve - Do you have an innate desire to rise to the next level, even after great accomplishment?
Creative Problem Solving and Probabilistic Thinking - You must enjoy learning and implementing new concepts quickly, combining knowledge from different domains to create new ideas, and take a data-driven and probabilistic approach to testing and implementing new ideas.
Team Mindset - We want people who understand 1+1 > 2 and are as committed to making the team better through sharing ideas as they are driven to improve their individual performance.
Mental Flexibility & Self Awareness - You’ll have to frequently adapt based on new data, results, and feedback on your trading ideas and your performance.
Orientation for Making Money - Although we value academic training, our work is not an academic exercise. We take a hacker’s approach to testing ideas, dropping projects that consume time without high upside, and focusing our next efforts on what will create the most value for the firm.
Research / Quant trading strategy skills to have or develop
Strong intuition and deep thinking with data sets - Designs new alphas, understands complex systems; knows where to start, or ask others where to start
Demonstrates strong “hacking” ability to quickly get into data to look for empirical relationships and decipher noise or signal
Familiarity with classical statistical methods and knows when and how to apply them in a rigorous fashion; Easily learns how to apply new statistical methods; will seek out and learn new methods to better solve problems
Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal
Experience in setup of research framework and execution of projects
Understanding of financial products, market dynamics, and microstructure
Low-level computer languages like C++ or Python, Java, etc.; awareness of strength in particular language and ability to solve more complex problems due to understanding nuances of the language