Quantitative Researcher - Machine Learning / AI

Company description

Radix Trading is a proprietary firm focused on quantitative research and scientific trading. We’re one of the most active liquidity providers on electronic exchanges globally, and have leveraged a culture of open, collaborative innovation to scale the reach of our ideas and pace of iteration, without having to scale our headcount (currently, we’re around 125 people across Chicago, New York, and Amsterdam).

In our industry, the vast majority of ideas will fail. So, since inception, we’ve focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than the day prior, glean insights from each idea, and leverage individual contributions to the fullest across our entire organization. 

We’re led by Ben Blander and Michael Rauchman, who played key roles in the rise of electronic trading, but both recognized a major gap in the industry — a true focus on research processes coupled with an open organizational structure that fosters collaboration. 

Job description

As a Quantitative Researcher, your focus is on furthering the ML developments of the firm and 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. 

As you get going, 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, and PhDs — not from other trading firms.  We seek mental diversity and add a select group of academics each year from a wide range of disciplines. In the last year new associates joined after completing academic commitments at MIT, Stanford, Harvard, Oxford, Princeton, UChicago, IAS, Columbia, and UPenn.

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

Why trading?

If you want to get near-immediate feedback on your best ideas, while leveraging cutting-edge technology, the trading industry is hard to beat. Every day we’re competing with some of the smartest, most driven people in the world trying to take our money -- and if we don’t stay at the very top of our game in research, technology, and economics, they will.

And while the highly-publicized wave of high-frequency or “flash” trading based on sheer speed of execution might have reached its limit, we see continued opportunities with our strategy of using statistical research to outsmart the competition.

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:

> PhD+ with experience with machine learning, deep learning, reinforcment learning 

> Persistent Drive to Improve - Do you have an innate desire to rise to the next level, even after great accomplishment? Our people have been very successful in their academic careers and are joining Radix to make even more impact.

> 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 - experience working with large, noisy data sets

> 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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* Please submit your CV/Resume & Transcripts (UG - unofficial copy is okay) to university@radix-trading.com to apply

**We will eventually require 2+ academic recommendation letters, but these are not required to apply upfront. Preference for existing letters, at least 1 being from your advisor --- these should be framed for academic positions, scholarships, research grants etc ---- no need to have your references write something new if letters already exist