Quantitative Researcher
We're looking for a Quant Researcher to join us remotely and take direct ownership of the research lifecycle, from raw data exploration all the way through to live strategy deployment. You'll work closely with our trading and engineering teams to develop, test, and continuously refine the models and algorithms that drive real P&L outcomes across crypto markets. This is a high-ownership role with meaningful exposure to live markets from day one. You won't be running support research in the background, you'll be a core contributor shaping the strategies we actually trade. That means working with large and often unconventional datasets, identifying exploitable statistical patterns in volatile and illiquid market conditions, and translating rigorous mathematical research into production-ready code that performs when it counts.
Responsibilities
- Develop, backtest, and continuously improve quantitative trading models and algorithms, translating mathematical insights into production-ready code
- Collaborate with trading and development teams to refine existing strategies and build new ones with a direct focus on P&L impact
- Work through large and complex datasets to identify patterns, validate hypotheses, and stress-test models against real market conditions
- Conceptualize valuation frameworks, improve mathematical models, and maintain strategy performance in live environments
- Conduct ongoing statistical research into crypto market microstructure and price dynamics using both conventional and unconventional data sources
Requirements
- Advanced degree in mathematics, statistics, physics, computer science, or a related quantitative field
- Strong proficiency in probability and statistics, including time-series analysis, machine learning, and pattern recognition applied to financial data
- Hands-on programming experience in Python, with familiarity in R or MATLAB and ideally some exposure to compiled languages like C++
- Prior experience in crypto markets, digital asset trading, or algorithmic finance
- Proven ability to work in a data-driven research environment with minimal direction
- Rigorous, evidence-based approach to problem-solving with the ability to move quickly from hypothesis to testable implementation