We are looking for a Quantitative Researcher to join a team focused on the development of fully automated systematic trading strategies across global equity and multi-asset markets.

The role sits at the intersection of research and engineering, with a strong emphasis on generating alpha signals, building predictive models, and translating research ideas into live trading strategies. You will be involved in the full lifecycle of strategy development, from initial hypothesis generation through backtesting, implementation, and ongoing performance refinement.

Key Responsibilities

1. Research and design predictive signals for systematic trading strategies.
2. Develop and test quantitative models using large-scale financial datasets.
3. Apply statistical and machine learning techniques to identify market inefficiencies.
4. Collaborate with engineers and researchers to implement strategies into production systems.
5. Monitor and analyse live strategy performance, iterating to improve returns and robustness.
6. Contribute to research infrastructure and tools that support scalable strategy development.
7. Take ownership of research projects with clear focus on PnL impact and real-world performance.

Requirements

1. Degree in a highly quantitative subject such as Mathematics, Statistics, Physics, Computer Science, Engineering, or similar.
2. Strong grounding in probability, statistics, and machine learning methods.
3. Excellent programming ability in Python, C++, or Java, with experience working on real datasets.
4. Demonstrated interest in systematic trading, quantitative finance, or algorithmic modelling.
5. Ability to work independently while collaborating effectively in a high-performance team environment.
6. Strong analytical mindset with a focus on practical, data-driven results.

Preferred Experience

1. Previous internship or work experience in quantitative research, systematic trading, data science, or related fields.
2. Experience building and deploying predictive models in production or research environments.
3. Familiarity with noisy, non-stationary datasets and financial time series data.