Quantitative Researcher (Signal Monetisation)

Company: A leading quantitative proprietary HFT firm expanding into mid-frequency strategies across global equities, futures, and derivatives markets.Location: LondonThe 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.ResponsibilitiesCombine 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.RequirementsAdvanced 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.