Quantitative Research Intern

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve.  Together we’re building a world-class platform to amplify our teams’ most powerful ideas.

Join a research team where curiosity meets scale. You’ll investigate foundational questions, uncover market insights and push the boundaries of what's possible - all with the support of near-limitless compute and world-class peers.

Take the next step in your career.

The role

• 10-week summer programme (22nd June to 28th August 2026)

• 09:00-17:30 working hours

• Based in Central London

Over the course of 10 weeks, G-Research Summer Research Programme interns gain a unique insight into life as a Quantitative Researcher at a leading quantitative finance research firm.

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 mathematical modelling, deep learning, optimisation, and machine learning 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.

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.

Top performers on the internship will be considered for full-time opportunities on completion of their studies.

Who are we looking for?

The ideal candidate will, at a minimum, have experience in the following areas:

• Strong background in mathematics

• Intermediate level of programming in at least one OO language (Python/C# desirable)

• An interest in applying data science, machine learning and optimisation techniques to real-world problems

• You should be in the final or penultimate year of a Masters or PhD in a highly technical or quantitative subject such as Mathematics, Physics, Statistics, Engineering or Computer Science. PhD preferred

• We also like to see active GitHub/Kaggle profiles, but these aren't a prerequisite

• 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.

Why should you apply?

• Highly competitive compensation plus accommodation

• G-Research community with weekly intern activities

• Lunch provided (via Just Eat for Business) and dedicated barista bar

• 30 days’ annual leave pro-rated

• Informal dress code and excellent work/life balance

• Central London office close to 5 stations and 6 tube lines

G-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas-driven business and we place great value on diversity of experience and opinions.

We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section