Quantitative Researcher

Quant Researcher, Mid-Frequency EquitiesFirm: Leading global proprietary trading firmAbout the firmOur client is a highly successful proprietary trading firm that trades only its own capital across global markets. It pairs world-class technology with a rigorous, scientific approach to research. Teams are small, collaborative and flat, and researchers have real ownership of strategies and a direct link to PnL.The roleYou’ll join a small, high-impact team building systematic equity strategies with holding periods from intraday to several days. The role covers the full research cycle: generating alpha ideas, building signals, constructing portfolios and modelling execution, then managing strategies once they’re live.What you’ll doResearch and develop predictive signals for global equities from price and volume, order book, fundamental, event-driven and alternative datasets.Build and improve portfolio construction, risk models and transaction-cost and market-impact models.Run rigorous, statistically sound backtests, guarding against overfitting, lookahead bias and data-snooping.Bring strategies into production and monitor live performance, attribution and decay.Find, evaluate and onboard new datasets to extend existing alphas and create new ones.Work closely with engineers and other researchers to improve shared research infrastructure.What we’re looking for2+ years in systematic equity research at a prop trading firm, quant hedge fund or similar. A track record of live, profitable strategies is strongly preferred.An advanced degree (PhD or MSc) in a quantitative field such as mathematics, statistics, physics, computer science or engineering, from a top university.Strong statistics and machine learning skills applied to noisy financial data: regression, time series, regularisation and cross-validation.Excellent Python skills (pandas, NumPy and the scientific stack). C++ or similar is a plus.A solid understanding of equity market structure, factor models, risk management and execution costs.Experience handling large, messy datasets and building robust research pipelines.An intellectually honest, PnL-focused mindset, independence and strong ownership.Nice to haveExperience with alternative data: sentiment, NLP, supply chain, web or app data.Experience across regions, such as the US, Europe and APAC.Experience with kdb+/q or distributed computing frameworks.Experience building portfolio optimisers or execution algorithms.What’s on offerHighly competitive compensation: market-leading base plus a significant discretionary or PnL-linked bonus.Direct ownership of strategies, with clear visibility of your contribution to PnL.Best-in-class data, computing resources and research infrastructure.A collaborative, non-hierarchical culture alongside some of the strongest researchers in the industry.[Benefits: relocation support, visa sponsorship, health cover, meals, etc.]