Learning And Training Third Party Risk Management in Amsterdam

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for 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, g