Quantitative Researcher - 2027 Graduates

Graviton Research Capital LLP

23h

About The Position

Graviton is hiring a Quantitative Researcher for 2027 graduates to investigate market behaviour, develop quantitative models, test hypotheses and turn insights into systematic trading strategies. Researchers work alongside technologists and take ideas from initial questions and experiments through validation and production. The problems are open-ended and involve large datasets, with research ideas able to move quickly toward live trading. Depending on the team and research interests, the role spans quantitative research and systematic trading. Researchers may discover predictive patterns and sources of alpha, investigate market microstructure, build predictive models and signals, conduct robust backtests, evaluate strategy performance, develop research infrastructure and AI-powered tools, and explore new techniques in machine learning, statistics and quantitative finance. Successful researchers discover sources of alpha and convert them into robust strategies, build models that perform under real market conditions, deepen understanding of market behaviour, improve team research frameworks, collaborate with technologists to bring ideas into production, and communicate complex quantitative ideas clearly. Researchers have meaningful ownership of problems from day one and work on specialized trading teams with significant autonomy.

Requirements

Degree in a quantitative discipline such as mathematics, physics, computer science, statistics, operations research or machine learning.
No active backlogs.
Strong programming skills in Python and/or C++.
Solid grounding in probability, statistics, linear algebra and optimization.
Experience in machine learning, statistical modelling or data analysis through coursework, research or personal projects.
Comfort working with large, complex datasets and strong analytical thinking.
Passion for applying quantitative thinking and rigorous research to open-ended problems in markets.
Excellent communication skills and ability to explain complex ideas clearly.

Nice To Haves

Prior exposure to quantitative research, algorithmic trading, market microstructure, competitive mathematics or machine learning.

Responsibilities

Discover predictive patterns and sources of alpha from market events.
Formulate hypotheses about market behaviour and design experiments to test them.
Apply probability, statistics, optimization and machine learning to build predictive models and quantitative signals for systematic trading.
Design robust backtests and statistical tests to distinguish genuine signals from noise.
Work with large and complex datasets to uncover patterns.
Investigate market microstructure and market behaviour across different timescales.
Evaluate strategy performance, identify weaknesses and refine models.
Develop research infrastructure and AI-powered tools and agents to accelerate experimentation and deepen analysis.
Work with technologists to translate research ideas into efficient, production-ready systems.
Explore techniques in machine learning, statistics and quantitative finance to uncover sources of trading edge.
Communicate complex quantitative ideas clearly and rigorously.