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G-Research is a leading quantitative research and technology company. Using the latest scientific techniques and advanced analysis methods, we find patterns in large, noisy data to produce world-beating predictive research.
Joining G-Research’s Summer Internship Programme, you will be given a meaningful and challenging research project that demands the application of innovative yet pragmatic mathematical and computational analysis.
Using rigorous scientific methodology, robust statistical analysis and pattern recognition you will extract meaningful predictive signals from financial time-series and use these to predict future dynamics.
Your project will give you the opportunity to use a wide range of techniques in areas such as machine learning, natural language processing, deep-learning and optimisation in a practical and challenging context. Additional work may involve the implementation of back-testing frameworks to ensure signal robustness or the creation of a pipeline which constructs and simulates the performance of a portfolio derived from various input signals.
For the duration of the internship, you will collaborate with one of our Quantitative Researchers who will act as a mentor as you work on your independent project. You will receive structured feedback and reviews to help you to improve and develop, culminating in a final presentation of your research ideas to senior management. Upon successful completion of the programme, many interns are offered the opportunity to join us full-time once they have completed their studies.
Taking part in G-Research’s Summer Internship Programme will give you an in-depth insight into our academic approach to the world of quantitative finance and allow you to explore the thriving city of London, while you get to know your fellow interns and colleagues through a full itinerary of fun social events.
The ideal candidate will, at minimum, have experience in the following areas:
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.