Overview

Quantitative research & machine learning roles in our Research Lab focus on applying deep mathematical, statistical and scientific rigour to tackle complex challenges in quantitative finance. We combine cutting-edge technology with world-class resources to create algorithmic platforms for our clients. Researchers analyse vast, complex datasets to uncover actionable insights, test hypotheses, build models and receive instant feedback to accelerate innovation. Advanced optimisation techniques are designed to extract maximum value from ideas.

We challenge conventional boundaries by applying state-of-the-art machine-learning techniques and massive compute power to stay ahead in a competitive environment. Innovation is essential; only novel approaches deliver an edge.

Machine Learning & College

We hire top ML practitioners and develop the next generation of talent through the G-Research Machine Learning College. Our researchers typically come from leading global institutions, often joining after PhDs or postdoctoral work, with publications at prestigious conferences. They are empowered with autonomy to shape their research in a collaborative, intellectually stimulating environment that values curiosity, creativity and deep thinking.

What our people say

There are recurring quotes and testimonials from researchers describing the open culture, freedom to pursue valuable directions, and the strong collaboration with colleagues across teams.

Role expectations

Thrive in a collaborative and dynamic environment where smart people learn and grow together. Our quantitative researchers and ML practitioners have a track record of academic achievement in mathematics, physics, ML, computer science or engineering. There’s no requirement for finance experience.

Responsibilities

• Conduct quantitative research and apply machine-learning techniques to real-world data within the finance domain.
• Design and implement advanced optimisation techniques to maximise value from ideas.
• Collaborate with engineers and other researchers to test hypotheses, build models and iterate rapidly.
• Contribute to the G-Research Machine Learning College and share knowledge with the team.
• Engage with a transparent, open culture and drive research directions autonomously where appropriate.

Qualifications

• Strong background in mathematics, statistics, physics, ML, computer science or engineering; track record of academic achievement or publications.
• Typically PhD or postdoctoral experience, or equivalent demonstrated research capability.
• Ability to apply rigorous scientific methods to testing hypotheses and building models; proficiency in programming and data analysis.

Interview process

• Stage two: technical and ML-focused interviews, with questions in mathematics, programming and statistics relevant to the role.
• Stage three: leadership interviews with some of the organization’s leaders, following successful technical rounds.

Online application

Quick and easy: submit your CV/resume and personal details. Our Talent Acquisition team reviews applications and provides an update within one week of applying. An interview preparation guide is provided, including quant quizzes and four interviews (usually four total, with ML-focused paths available).

Additional information: you may be asked to complete a quantitative aptitude assessment or an ML-specific assessment depending on your background.

Locations and opportunities

Open roles are available in our global locations. Look for current postings and apply when ready.

We partner with and invest in the open-source community, identify and test the latest technology, and onboard researchers and engineers to keep innovating.