Tempest Vane Partners
Junior Quantitative Researcher - Machine Learning - Hedge Fund
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The Client
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.
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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.
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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.
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