Quantitative Researcher

About BlockTech

BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance.

Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further.

The role

This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction.

You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on.

What you'll do

• Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML

• Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely

• Own models in production: monitor live performance, diagnose decay, and iterate on what you ship

• Set research direction alongside traders, deciding which trades are worth making and why

• Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour

What we're looking for

• 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live

• A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar)

• 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 and how a model works, not just that it does

• The judgement to prioritise research bets independently and the communication skills to align researchers, engineers, and traders

• Strong software engineering instincts: production-grade code that others can read, test, and run