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    Quantitative Researcher - Chicago, United States - Radix Trading University Job Board

    Radix Trading University Job Board
    Radix Trading University Job Board Chicago, United States

    3 weeks ago

    Default job background
    Full time
    Description


    As a Quantitative Researcher, 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.


    By working on a variety of projects with different collaborators over a six-month period, you'll gain new knowledge and insight into the fundamentals of market dynamics, trading strategies, and our proprietary research platform.

    We've stripped away the boundaries of what you can access and touch internally, so while you learn the intricacies of our industry, you'll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team.

    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.


    Over the past year new associates have joined our research team from: Princeton, Harvard, Stanford, MIT, IAS, Penn, UChicago, Columbia, and Oxford.


    COMPENSATION – Competitive salary, plus quarterly bonus based on individual performance and contribution towards success of others and the firm.

    Qualifications


    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 problem - -Experience with modern AI techniques and methods or desire to work on Applied Machine Learning Problems a plus

    > 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

    #J-18808-Ljbffr
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