Summer Research Programme 2017

Quantitative Research London, United Kingdom


Description

G-Research is a leading quantitative research and technology company. We use scientific techniques, big data and world-class technology to predict future movements in financial markets, and develop the platform to deploy these ideas globally. We offer a dynamic, flexible and highly stimulating environment where good ideas are prized and rewarded.

What does the programme entail?


Quants at G-Research work investment strategies to predict price returns in financial markets across multiple asset classes. The primary responsibility of the role involves seeking patterns in large, dirty and noisy data sets, using techniques such as time series analysis, probability theory and regression analysis.

For the duration of the programme, you will be paired with one of our Quants who will act as a mentor as you work on a project. Our aim is to accurately reflect what a full time role as a Quant would look like day-to-day, and enable you to experience the analytical methods we use to carry out research.

Over the course of the Summer Research Programme you will also receive structured feedback and reviews to enable you to improve and develop, culminating in a final presentation to senior management of your research ideas. We are also a very social firm, and you will have a full itinerary of social and networking events with peers throughout the Programme. Upon successful completion of the programme, you may be offered the opportunity to join us full-time once you have completed your PhD.

We offer a highly competitive salary for the duration of the programme, and working hours of 9-6pm.

Core requirements:

  •  A strong background in mathematics is a must. You must already be familiar with the advanced mathematics required for financial modelling, such as probability theory, time series analysis and regression analysis.
  • Ideally you will be in your final or penultimate year of your PhD in a highly numerable subject such as mathematics, physics, statistics, engineering and computer science. Students from other subjects using quantitative methods and in earlier years of their PhD are welcome to apply however.
  • Experience working with large data sets is also valuable.
  • What is essential is practical, hands-on ability to apply mathematical concepts to real world financial problems, to implement theoretical insights as working code, and the ability to work independently in a research environment.

Previous experience in finance is not required, although an interest in finance and the motivation to rapidly learn more is a prerequisite for working here.