Quantitative Researcher
4 weeks ago
As a Quantitative Researcher at Radix Trading, you'll be at the forefront of identifying trading opportunities and driving innovation. With a strong foundation in quantitative skills and coding proficiency, you'll accelerate the research process and help others leverage your work.
Over a six-month period, you'll collaborate on various projects with a diverse team of academics and professionals, gaining valuable knowledge and insight into market dynamics, trading strategies, and our proprietary research platform.
With a focus on mental diversity, our team welcomes individuals from a wide range of disciplines, including academia. Recent additions to our research team have come from esteemed institutions such as Princeton, Harvard, Stanford, and Oxford.
Competitive Compensation
We offer a competitive salary, plus a quarterly bonus based on individual performance and contribution to the success of others and the firm.
Key Qualifications- Persistent Drive to Improve: Demonstrate a desire to rise to the next level, even after great accomplishment.
- Creative Problem Solving and Probabilistic Thinking: 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: Understand the value of collaboration and are committed to making the team better through sharing ideas.
- Mental Flexibility & Self Awareness: Frequently adapt based on new data, results, and feedback on your trading ideas and your performance.
- Orientation for Making Money: Take a hacker's approach to testing ideas, dropping projects that consume time without high upside, and focusing efforts on what will create the most value for the firm.
Research / Quant Trading Strategy Skills
- Strong intuition and deep thinking with data sets: Design new alphas, understand complex systems, and know 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.
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