Monetisation Quant: Alpha Blending & Live PnL

Thurn Partners; Greater London; On-site; GBP 150,000 - 230,000; Full time

Job summary

Thurn Partners, London-based quantitative trading firm, is building a specialist team focused on alpha blending, monetisation, and optimisation. You will work with a library of raw signals from the alpha research group to produce live, risk-bearing strategies.

You will own the live optimisation, risk, and execution interfaces, iterating against PnL, adjusting signal weights, and refining capital efficiency in production settings.

Qualifications

• Strong background in statistical modelling and ML, with optimisation and portfolio construction.
• Experience in signal combination or systematic portfolio construction, ideally in a mid-frequency setting.
• Proficiency in Python; C++ and HPC experience are a plus.
• Track record of taking research into production and generating live PnL.
• Experience with financial time-series analysis and market microstructure is preferred.

Responsibilities

• Combine and weight a large set of raw alpha signals into coherent strategies.
• Build and own the optimisation layer: portfolio construction and capital allocation.
• Model and minimise trading costs, considering market impact and capacity constraints.
• Iterate on live performance: monitor PnL, diagnose alpha decay, rebalance weightings.
• Work with infrastructure and execution teams to deploy the strategies in production.
• Own the live risk profile of the blended book and manage exposures.

Skills

Statistical modelling

Machine learning

Portfolio construction

Python

C++

Productionization

Time-series analysis

Market microstructure

Job description

Thurn Partners, London-based quantitative trading firm, is building a specialist team focused on alpha blending, monetisation, and optimisation. You will work with a library of raw signals from the alpha research group to produce live, risk-bearing strategies.

You will own the live optimisation, risk, and execution interfaces, iterating against PnL, adjusting signal weights, and refining capital efficiency in production settings.