• A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience
• Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas)
• A deep, intuitive grasp of overfitting, generalisation, validation design, and feature engineering - you can explain why a model works, not just that it does
• Solid software engineering instincts: you write code that other people can read, test, and run in production
• Genuine curiosity about financial markets and market microstructure - prior finance experience is welcome but not required
• Clear written and verbal English, and the ability to communicate complex ideas to a mixed audience of researchers, engineers, and traders
• Hands-on experience building and deploying ML models, ideally on time-series, forecasting, or anomaly detection problems
• Familiarity with crypto markets, derivatives pricing, and/or high-frequency trading data