Thurn Partners
Quantitative Researcher (Signal Monetisation)
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Company: A leading quantitative proprietary HFT firm expanding into mid-frequency strategies across global equities, futures, and derivatives markets. Location: London
The role: The firm is building a specialist team focused on alpha blending, monetisation, and optimisation. The team works with a library of raw signals from the alpha research group to produce live, risk-bearing strategies, with exposure from signal combination up to execution.
Responsibilities
- Combine and weight a large set of raw alpha signals into coherent, tradable strategies, managing signal correlation, overlap, and interaction.
- Build and own the optimisation layer: portfolio construction, capital allocation, and position sizing across signals and markets.
- Model and minimise the cost of trading, accounting for market impact, transaction costs, and capacity constraints when translating signals into positions.
- Iterate on live performance: monitor PnL, diagnose alpha decay, rebalance signal weightings, and improve the capital efficiency of the book over time.
- Work with infrastructure and execution teams to deploy the combined strategies into production and refine them under live conditions.
- Own the live risk profile of the blended book, conducting rigorous risk assessment and managing exposures.
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Requirements
- Advanced degree (PhD or MSc) in a quantitative discipline: Mathematics, Physics, Statistics, Computer Science, or similar.
- Strong background in statistical modelling and machine learning, with particular value placed on optimisation, ensemble methods, and portfolio construction (e.g. convex optimisation, mean-variance and its extensions, gradient boosting, neural networks).
- Demonstrable experience in signal combination, alpha mixing, or systematic portfolio construction, ideally in a mid-frequency setting.
- Proficiency in Python; C++ and experience in high-performance computing environments are a plus.
- A track record of taking research into production and generating live PnL is highly valued.
- Experience with financial time-series analysis, market microstructure, or transaction cost modelling preferred.
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