Quantitative Developer - Python / Commodities

The quant development team sit on the desk and create, implement and support the tools, frameworks, platforms and models for the traders, researchers and quants. Due to increased research into Commodities, they are looking for a Quant Developer to convert unprecedented research theories into tangible models and platforms, written in reliable and scalable production standard code. This is part of an invigorating project to re-modernise the firm's Commodity Risk and Trading platform.

Your Role:

Design and develop systems for conducting Commodities research and real-time alpha generation.

Build bespoke frameworks for exploring, designing and deploying systematic strategies and signals.

Increase research velocity and create back-testing and simulation frameworks which ensure seamless strategy implementation.

Utilise machine learning algorithms for analysing vast alternative datasets.

Build desktop tools for PnL estimates and position monitoring.

Experience Required / About You:

Strong academic background with a Bachelors or Masters degree in Computer Science or related discipline(s).

Commercial experience working collaboratively, building relevant systems and implementing strategies.

Strong Python development skills.

Superb analytical and quantitative skills; linking business problems with highly technical solutions.

The ability to find innovative ways around convoluted mathematical and computer science problems.

Further Context:

The firm has an excellent culture and is one of the most reputable in the industry, with a track record of delivering advanced custom tech and constant innovation.

The company invests heavily in its people and their environment - it's a flat structure with scope for trajectory. You're never more than two steps away from the CTO.

This is a permanent position with a highly competitive salary and a comprehensive benefits package. Please enquire for more details.

The framework feeds the models with huge volumes of alternative datasets to accurately track and capitalize on the live supply and demand for Power and Gas.

The markets of both products are growing in complexity each day. While increased fracking in Europe and the US, Liquid gas technology propelling shipping of gas from Qatar and even the threat of pirates is complicating predictions around the supply and demand of gas, the fall of electrical demand during the pandemic and the co-dependence of economic growth and energy demand make it all the more difficult to identify shifts in the Power market. All these factors are driving the evolution of increasingly complex mathematical and statistical models and the need for them to seamlessly interact with one another. The quantitative and engineering challenges to solve here are immense.