About the Role
You'll learn the scientific craft behind systematic trading, working with senior researchers to take ideas from hypothesis through rigorous backtesting to production — mining large-scale market data for predictive signals and structural inefficiencies.
About the Internship
- Duration: 3–6 months (flexible start; full-time preferred)
- Location: Our Singapore office, our New York office, or remote
- Who it's for: Students and early-career researchers with a strong quantitative background and a scientific mindset.
What You'll Do
- Research and backtest trading signals using statistical and machine-learning techniques.
- Clean, transform, and analyse large market datasets to surface predictive features.
- Investigate market microstructure and exchange dynamics with rigour and honesty.
- Contribute to portfolio construction and execution research aimed at better risk-adjusted returns.
- Document findings clearly and present them to traders and fellow researchers.
- Pair with developers and traders to deploy validated research into live systems.
What You Bring
- Pursuing or recently completed a degree in a quantitative field (Mathematics, Physics, Statistics, Computer Science, or related).
- Solid programming skills in Python (PyTorch, TensorFlow, or scikit-learn a plus).
- A strong grasp of probability, statistics, and time-series analysis.
- Creativity, scientific rigour, and comfort with ambiguity and large datasets.
- Self-direction and a genuine enthusiasm for research.
- Fluency in English is required.
What You'll Get
- Access to proprietary datasets and strong computing infrastructure.
- Mentorship from researchers with deep experience in quantitative finance.
- Competitive, performance-linked compensation.
- A collaborative research culture built on innovation and intellectual honesty.
- A clear path to a full-time role for strong performers.
- Flexibility to work from our Singapore or New York office, or remotely.