What we do
At NMRQL we use and develop state-of-the-art machine learning algorithms to predict the stock market using a variety of structured and unstructured data sources. These predictions are then used to construct optimal investment portfolios on behalf of our clients. We call this approach 'computational investing'. The benefits of computational investing for our clients is that our models are testable, and 100% non-emotional.
Why Work For Us
NMRQL is not your average investment company. We don't do VBA and we don't do Windows unless we really, really have to. Our models are developed and tested in Python 3 and deployed primarily on Unix servers. More importantly, our entire development process is geared towards computational efficiency and flexibility.
To put it frankly, this means that at nmrql you will get to flex your 'computer science muscles' on truly interesting architectural and code problems - think distributed computing, concurrency, design patterns, data structures, advanced algorithms, and applied mathematics. You won't be writing 'boiler plate code' at NMRQL.
NMRQL is also very competitive as far as compensation goes and employees get to share in the out performance of the fund through a competitive bonus scheme.
NMRQL is a meritocracy; the best ideas are the ones that win at the end of the day. We promote an open and honest culture. As a company we are not averse to trying new things. We are scientists at heart.
We are also committed to giving back to the open source community and hope to open source our first research package - a library for performing Tensor Network Decomposition's - before the end of this year (2017).
Our Engineering Processes
Due to the exploratory nature of our work and the fact that we are our own clients, we don't do strict requirements gathering. Instead we prefer to start with the broad strokes and fill in the details as we go along.
All code is peer reviewed and simulation results are "stress tested" extensively before being used in the fund. Our code is hosted on GitHub.com and we opt to follow the "gitflow" branching model. We target monthly releases.
Our Hiring Process
Candidates will be asked to complete a take-home assignment and, if they pass, will be required to do a technical interview. The technical interview will focus on raw coding ability and higher-level problem solving skills. In addition to this the candidate will be invited to do some interpersonal interviews.