Position Overview

We are looking for a Quantitative Research Analyst to investigate, design, and evaluate systematic trading strategies using statistical methods, computational research, and market data analysis. The role focuses on developing testable hypotheses, identifying potential sources of market inefficiency, and assessing whether proposed strategies can perform reliably after accounting for transaction costs, execution constraints, and changing market conditions.

Working alongside quantitative developers, traders, and market data specialists, the analyst will contribute to the research lifecycle from initial idea generation through backtesting, validation, and ongoing performance monitoring. The position requires a disciplined scientific approach, strong coding skills, and an understanding of the practical differences between simulated results and live trading performance.

This opportunity is suited to a researcher who is interested in algorithmic trading and wants to apply advanced statistics, machine learning where appropriate, and rigorous experimentation to financial markets.

Key Responsibilities

• Research systematic trading strategies across relevant asset classes, including foreign exchange, equities, exchange-traded products, or other liquid financial instruments.
• Formulate testable hypotheses using market microstructure concepts, statistical relationships, momentum, mean reversion, or other established quantitative approaches.
• Develop research prototypes and analytical experiments using Python and appropriate scientific computing libraries.
• Construct reliable historical datasets and investigate data quality, survivorship bias, look-ahead bias, and timestamp inconsistencies.
• Design and run backtests that account for transaction costs, spreads, slippage, turnover, liquidity constraints, and realistic execution assumptions.
• Evaluate strategy performance using metrics such as Sharpe ratio, maximum drawdown, volatility, hit rate, and risk-adjusted returns.
• Perform robustness testing, sensitivity analysis, out-of-sample validation, and walk-forward analysis to evaluate strategy stability.
• Investigate the impact of different market regimes on strategy performance and assess the risk of overfitting.
• Collaborate with quantitative developers to translate validated research into efficient, maintainable production code.
• Analyse execution data to identify implementation shortfalls and opportunities to improve trading efficiency.
• Maintain clear records of research assumptions, experiments, model versions, and evaluation results.
• Monitor approved strategies and investigate material deviations between expected and observed performance.

Candidate Requirements

• Master’s degree or PhD in Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, Econometrics, or a related quantitative discipline.
• At least two years of relevant experience in quantitative research, systematic trading, financial data science, or statistical modelling.
• Advanced Python skills and experience with pandas, NumPy, SciPy, or comparable analytical libraries.
• Strong understanding of statistical testing, time-series analysis, probability, optimisation, and experimental design.
• Practical experience with backtesting frameworks, trading data, or systematic strategy evaluation.
• Understanding of trading costs, market liquidity, execution quality, and the limitations of historical simulations.
• Ability to write clear, maintainable research code and use version control effectively.
• Experience with machine learning methods is advantageous, provided the candidate understands their validation and generalisation limitations.
• Strong research discipline, numerical accuracy, and the ability to communicate findings through evidence-based reports.
• Familiarity with SQL, Linux, and cloud-based analytical environments is beneficial.

Research and Trading Environment

The role may involve Python, Jupyter, SQL, Git, historical market data feeds, backtesting systems, performance analytics, and research databases. Collaboration with trading infrastructure teams may also involve execution logs, market data processing, and strategy deployment workflows.

What This Opportunity Offers

• Ownership of meaningful quantitative research projects.
• Opportunities to evaluate systematic strategies using rigorous statistical methods.
• Close collaboration with quantitative developers and trading professionals.
• Exposure to the full research lifecycle, from hypothesis development to performance monitoring.
• Professional development in systematic trading, market microstructure, and financial research methodology.

Ideal Candidate

The ideal candidate is curious, technically rigorous, and comfortable challenging promising results. They understand that a successful backtest is not sufficient evidence of a profitable strategy and consistently account for costs, uncertainty, data quality, and changing market conditions when evaluating research outcomes.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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