About the Role
the company is seeking a Quantitative Researcher to contribute to the research, development, and improvement of systematic investment strategies across global financial markets. The role combines statistical analysis, machine learning, and financial research to identify predictive signals and translate them into robust investment strategies. You will work closely with investment and engineering colleagues across the research lifecycle, from data exploration and hypothesis development to backtesting, implementation, and ongoing performance evaluation.
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Key Responsibilities
- Develop and test investment hypotheses using financial, economic, and alternative datasets.
- Research predictive signals and systematic strategies across asset classes, including equities, fixed income, foreign exchange, and commodities.
- Apply statistical methods, time-series analysis, and machine learning to modelling financial markets.
- Source, clean, and evaluate datasets, addressing data quality issues and ensuring their suitability for research.
- Design rigorous backtests and out-of-sample evaluations, accounting for transaction costs, liquidity, implementation constraints, and potential research biases.
- Assess strategy robustness across market conditions, identifying overfitting, data leakage, and sensitivity to modelling assumptions.
- Contribute to portfolio construction, risk modelling, and optimisation, including diversification, position sizing, and turnover management.
- Collaborate with engineers to translate research into reliable production implementations.
- Monitor model and strategy performance, investigate deviations from expectations, and propose improvements.
- Maintain clear research documentation, reproducible code, and experiment records.
- Present findings, assumptions, and limitations clearly to investment and technical colleagues.
- Protect confidential information and intellectual property, and follow the firm’s research, risk, and compliance policies.
Qualifications and Skills
- A degree in mathematics, statistics, computer science, physics, engineering, quantitative finance, or a related discipline.
- Strong foundations in probability, statistics, and quantitative analysis.
- Proficiency in Python and relevant tools for data analysis, statistical modelling, and machine learning.
- Experience working with large datasets and building reproducible analytical workflows.
- Understanding of research validation, including time-series cross-validation, hypothesis testing, and the risks of multiple testing.
- Familiarity with financial markets and systematic investing, or a demonstrated ability to apply quantitative methods to complex problems.
- Strong problem-solving skills, attention to detail, and the ability to challenge assumptions.
- Clear communication and the ability to work independently and collaboratively.
Desirable Experience
- A master’s degree or PhD in a relevant quantitative discipline.
- Experience in quantitative investment research, systematic trading, or applied machine learning.
- Knowledge of futures markets and multi-asset portfolio construction.
- Experience with Bayesian methods, optimisation, deep learning, or other advanced modelling techniques.
- Familiarity with SQL, version control, software testing, and cloud computing.
- Experience supporting the deployment and monitoring of models in production.
What Success Looks Like
Success means producing well-supported, reproducible research that improves investment decisions. This includes identifying useful signals, rejecting unreliable hypotheses, strengthening portfolio robustness, and helping translate research into dependable trading processes.
