G-Research is Europe’s leading quantitative finance research firm. We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in financial markets.
10 week summer programme (July to September 2022)
09:00-17:30 working hours
Based in Central London
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.
Our full-time ML researchers have the opportunity to use a range of ML tools in an applied setting, putting their ML expertise to use in direct, production-ready applications with immediate results. They have access to vast computing resources and are limited only by their imagination. As an ML intern you will have the opportunity to experience some of this as part of a 10 week programme working on a complex and interesting ML problem.
You will be paired with a mentor who will supervise your work and provide ongoing feedback to help you improve and develop, as well as access to senior staff who are leaders in their fields. Your internship will culminate in a final presentation of your research ideas to senior management and 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.
Either a post-graduate degree in machine learning or a related discipline, or commercial experience developing novel machine learning algorithms. We will also consider exceptional candidates with a proven record of success in online data science competitions (e.g. Kaggle)
Experience in one or more of deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inference
Excellent reasoning skills and mathematical ability are crucial: off the shelf methods don’t always work on our data so you will need to understand how to develop your own models
Strong programming skills and experience working with Python, Scikit-Learn, SciPy, NumPy, Pandas and Jupyter
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
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