Quantitative Researcher - Postgraduate
At a glance
Mid-level Data Science role at G-Research. London · full-time · no salary stated · we estimate £70,750–£131,900.
G-Research has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Use statistical analysis and machine-learning techniques to forecast movements in financial markets
- Develop and test research ideas against real-world data in a research-focused setting
- Apply substantial computing resources to investigate market data and build predictive approaches
What you bring
- You hold, or are pursuing, a master's or doctorate in a quantitative discipline such as mathematics, statistics or computer science
- You are interested in using mathematical ideas to solve practical financial questions
- You can turn theoretical findings into working code and are motivated to learn about finance quickly
Who this fits
This role suits a postgraduate researcher interested in quantitative finance and machine learning. It is based in London and focuses on individual research work.
From the employer
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
Our researchers use the latest scientific techniques and advanced statistical analysis methods to predict movement in global financial markets.
This requires them to harness massive compute power and use state-of-the-art ML techniques to find innovative solutions, as textbook methods won’t beat the competition.
This is a pure research role where you will be able to develop and test your ideas with real-world data in an academic environment.
Machine Learning College
ML College is an established, in-house learning programme at G-Research. It’s designed to develop our researchers into fully-fledged machine learning practitioners through a world-class, custom curriculum.
ML College is exclusive to new and existing Quantitative Researchers at G-Research, so if you join us in this role you’ll be able to take advantage of a learning experience tailored to accelerate your knowledge and expertise in machine learning quickly and effectively.
Find out more about the G-Research ML College.
Who are we looking for?
The ideal candidate will have:
- An interest in applying mathematical concepts to real-world financial problems
- An interest in implementing theoretical insights as working code
- A Masters or PhD degree (or be working towards one) in a highly quantitative subject, such as mathematics, statistics, computer science, physics or engineering
- Previous financial experience 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 annual discretionary bonus
- Lunch provided (via Just Eat for Business) and dedicated barista bar
- 35 days’ annual leave
- 9% company pension contributions
- Informal dress code and excellent work/life balance
- Comprehensive healthcare and life assurance
- Cycle-to-work scheme
- Monthly company events
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
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