Junior Quantitative Researcher
We are seeking a talented Equities Junior Quantitative Researcher to join a successful trading team at a tier‑1 hedge fund in Zug. The team trades systematic global equities strategies, with holding periods ranging from intraday up to a week. This includes a strong focus on statistical arbitrage (stat‑arb) and long/short market‑neutral strategies.
You will use advanced quantitative techniques to enhance existing frameworks and drive further improvements in algorithmic trading performance. Following several highly successful years, the team is expanding and seeking a researcher who can contribute innovative ideas and support the full research cycle-from idea generation through to implementation.
The ideal candidate will have 1-3 years of experience in equity strategy research and development, alpha generation, and will be proficient in both Python and C++.
Key Responsibilities:
- Develop and implement systematic trading strategies focused on global equity markets, including statistical arbitrage and long/short market‑neutral approaches.
- Conduct rigorous quantitative research to identify new trading opportunities and enhance existing models.
- Perform backtesting and statistical analysis to validate, refine, and optimise strategies.
- Monitor and analyse market trends, alternative datasets, and relevant microstructural information to support trading decisions.
Qualifications:
- 1-3 years of experience within the quantitative equity space, ideally with exposure to statistical arbitrage or long/short market‑neutral strategy development.
- Hands‑on experience researching, designing, or improving equity alphas, including cross‑sectional or short‑horizon signals.
- Strong programming skills in C++ and Python.
- Proficiency in statistical analysis, quantitative modelling, and time‑series or cross‑sectional research methods.
- Experience with backtesting frameworks, large‑scale datasets, and data analysis tools.
- Strong analytical and mathematical background, with the ability to evaluate the robustness and performance of trading strategies.
- Excellent problem‑solving abilities, attention to detail, and a systematic approach to research.
- Ability to work independently as well as collaboratively within a small, fast‑paced trading team.
Preferred Qualifications:
- Advanced degree (Master's/PhD) in Mathematics, Statistics, Computer Science, Engineering, Physics, or a related quantitative discipline.
- Experience with machine learning techniques or feature engineering applied in the context of systematic trading.
- Familiarity with equity markets, risk modelling, or macroeconomic factors that influence equity behaviour.
- Prior experience contributing to or maintaining stat‑arb or long/short equity strategies is a plus.
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