Quantitative Trader

19 hours ago

Slough Berkshire, United Kingdom Durlston Partners Full time

Summary A quantitative trader with several years of experience in a small, high-ownership pod structure, covering the full lifecycle of research, modelling, implementation, and daily trading decision-making. Strong record of generating new alpha ideas, improving model efficiency, and contributing to systematic trading strategies across global equities and related products. Core Strengths 1. Full-Stack Quant Experience End-to-end exposure across research, quant development, and portfolio/trading decisions. Comfortable with tight execution loops and taking ownership of full model pipelines. Experienced in debugging production strategies and improving robustness. 2. Alpha & Feature Innovation Regular contributor of new features, signals, and ML-driven model improvements. Skilled at evaluating new data sources and optimising existing input pipelines. Experience with feature engineering, cross-validation techniques, and model diagnostics. 3. Market Awareness & Risk Sensitivity Background in systematic long/short equities across US and Europe. Additional exposure to fixed income and market-making style risk management. Strong intuition for differentiating model-driven losses vs. risk-management errors. 4. Technical Skills Strong programming background (Python, C++/C#, or similar). Experience with production-grade ML workflows. Familiar with distributed compute, model optimisation, and low-latency considerations. 5. Small-Team Versatility Works in a 4-person pod—responsible for everything from research to deployment. Able to operate independently with minimal structure. Thrives in environments where decisions are fast, data-driven, and collaborative. Trading & Research Focus Strategies: Systematic L/S equities, with some exposure to fixed-income signals and hedging activities. Style: Medium- to high-frequency stat-arb ideas (non-HFT). Daily Activities: Monitoring model outputs Intraday adjustments to risk Evaluating PnL drivers Running daily research iterations Implementing improvements to execution logic Practical Achievements Delivered multiple incremental improvements to alpha and risk models. Designed or co-designed new ML-based components that fed directly into PnL improvements. Improved data pipelines and feature computation speed, increasing research efficiency. Helped reduce risk-related drawdowns by identifying and correcting model sensitivities. Motivations Seeking a more structured, high-performance market-making environment like Optiver. Wants to work with stronger PMs/traders and avoid bottlenecks introduced by inconsistent risk management.

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