Quantitative Researcher
Role Overview
Join a small, high-impact team building systematic equity strategies with holding periods from intraday to several days.
Own the research cycle from alpha ideas and signal development through portfolio construction and execution modeling to managing live strategies.
The firm trades its own capital across global markets and emphasizes scientific research, collaboration, and researcher ownership.
Key Skills for This Role
Full Job Posting
The role
Join a small, high-impact team building systematic equity strategies with holding periods from intraday to several days.
Own the research cycle from alpha ideas and signal development through portfolio construction and execution modeling to managing live strategies.
The firm trades its own capital across global markets and emphasizes scientific research, collaboration, and researcher ownership.
What you’ll do
- Research predictive signals for global equities using market, fundamental, event-driven, and alternative datasets.
- Build and improve portfolio construction, risk, transaction-cost, and market-impact models.
- Run rigorous backtests while guarding against overfitting, lookahead bias, and data-snooping.
- Deploy strategies and monitor live performance, attribution, and decay.
- Evaluate and onboard datasets to extend existing alpha and create new strategies.
- Collaborate with engineers and researchers to improve shared research infrastructure.
What we’re looking for
- At least 2 years of systematic equity research experience at a proprietary trading firm, quantitative hedge fund, or similar.
- A track record of live, profitable strategies is strongly preferred.
- 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, including regression, time series, regularisation, and cross-validation.
- Excellent Python skills, including pandas, NumPy, and the scientific stack; C++ or similar is a plus.
- Solid understanding of equity market structure, factor models, risk management, and execution costs.
- Experience with large, messy datasets and robust research pipelines.
- Intellectual honesty, a PnL-focused mindset, independence, and strong ownership.
Nice to have
- Experience with alternative data such as sentiment, NLP, supply chain, web, or app data.
- Experience across regions such as the US, Europe, and APAC.
- Experience with kdb+/q, distributed computing, portfolio optimisers, or execution algorithms.
What’s on offer
- Highly competitive market-leading base compensation plus a significant discretionary or PnL-linked bonus.
- Direct ownership of strategies and visibility into contribution to PnL.
- Best-in-class data, computing resources, and research infrastructure.
- Collaborative, non-hierarchical culture alongside experienced researchers.
- Benefits listed include relocation support, visa sponsorship, health cover, and meals.
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