Junior Quantitative Researcher - Machine Learning - Hedge Fund

My client is a highly successful quantitative trading firm headquartered in London. The business has an exceptional long-term track record of developing systematic strategies across multiple asset classes, geographies and trading horizons.

They are looking for a talented junior Quantitative Researcher with a strong background in predictive machine learning to join one of their established research teams. This is an outstanding opportunity for an early-career researcher to apply advanced statistical and machine-learning techniques to complex financial data in a highly collaborative, research-led environment.

What You'll Get

• An opportunity to begin your career at one of London's most successful and highly regarded quantitative trading firms.
• The chance to work alongside exceptional quantitative researchers, machine-learning specialists and software engineers.
• A highly collaborative environment with a strong emphasis on mentoring, learning and intellectual development.
• Access to industry-leading proprietary datasets, research tools and computing infrastructure.
• The freedom to conduct original research and explore innovative modelling techniques.
• Exposure to the complete strategy-development lifecycle, from initial hypothesis through to live trading.
• Excellent career progression, with the opportunity to take increasing ownership of research projects and systematic strategies.
• A market-leading compensation package, including a generous base salary and performance-related bonus.
• A comprehensive benefits package, including pension, private healthcare and life assurance.

What You'll Do

• Conduct original research into the application of predictive machine learning within systematic trading.
• Analyse large, complex and noisy datasets to identify patterns capable of forecasting financial-market behaviour.
• Develop, train and validate statistical and machine-learning models for return prediction, signal generation and market forecasting.
• Research techniques including supervised learning, regularisation, feature selection, representation learning and ensemble modelling.
• Design robust experiments and backtests that account for overfitting, non-stationarity, transaction costs and changing market conditions.
• Investigate new datasets and develop features that improve the predictive performance of existing models.
• Work closely with experienced quantitative researchers and engineers to implement successful models within the firm’s production research and trading systems.
• Monitor model behaviour and investigate opportunities to improve performance, robustness and scalability.
• Stay current with relevant developments in machine learning, statistics and quantitative finance.

What You'll Need

• A Master's or PhD from a leading university in Machine Learning, Computer Science, or another STEM discipline.
• Strong knowledge of modern machine-learning methods and the mathematical principles underlying them.
• Experience developing predictive models through academic research, internships or an early-career role.
• A rigorous understanding of statistics, probability, experimental design and model validation.
• Experience working with large, complex or high-dimensional datasets.
• Strong programming skills in Python and familiarity with relevant numerical and machine-learning libraries.
• The ability to translate theoretical ideas into carefully designed empirical research.
• A genuine interest in applying machine learning to financial markets; previous professional finance experience is advantageous but not essential.