Quant Developer - Python, NumPy, SciPy, Pandas, Linux, Open source
A small, modern hedge fund that has heavily influenced systematic trading since the late 1980s. The company promotes the use of open source technology and collaboration. Their flat-structured, transparent nature allows developers to rotate between teams so every developer has a thorough understanding of the systems they are operating on. They see no limits and are actively looking for systematic trading opportunities around the world.
They employ developers from a variety of different professional backgrounds, e.g. computer science, mathematics, science and finance. The company pride themselves on the quality of their employees and actively seek to attract and retain the best people. They are very proactive in the wider community, hosting meet-ups, funding donations for many trust organisations and sponsoring awards for the educational system across the UK. The Role
Working very closely with Quant Researchers. Quant developers can expect an array of challenges from implementing new trading strategies to building risk analysis tools.
The majority of the company's systems run on Linux and most of their code is Python. Experience with Python is not fundamental; strong technical experience and high-quality coding abilities in any OO will be sufficient. Evidently, developers would have to be keen to cross-train.
A financial background is not imperative, but developers are expected to have an active interest in, and an understanding of financial markets, etc. Requirements:
Keywords: Hedge fund, open source, Python, Linux, finance, NumPy, SciPy, Pandas
- Strong academic background from one of the following: Computer Science, Mathematics, Engineering or Physics, or equivalent
- Strong coding experience (ideally 1+ yr) in any OO
- Personal passion for technology, e.g. open-source involvement, attending meet-ups, personal projects, etc.
- Exposure or experience using Python and Linux systems would be preferred, but is not compulsory
- Exposure or experience using NumPy/SciPy/Pandas would be preferred, but is not compulsory