About the Job
We're building a next-gen quantitative trading and research platform focused on systematic, data-driven alpha generation across crypto markets. This role is for researchers who design, test, break, and rebuild their own trading strategies from first principles. You will research, design, backtest, and iterate original crypto trading strategies across spot, perpetuals, and derivatives, working closely with engineering and trading to convert ideas into production-ready systems. This is not a signal copying or indicator-tuning roleif you've never built your own strategy end-to-end, this role is not for you.
Key Responsibilities
- Design original crypto trading strategies including momentum, mean reversion, market structure, volatility, funding, and microstructure approaches
- Build and maintain backtesting pipelines using historical crypto data (tick, OHLCV, funding, order book)
- Perform rigorous statistical evaluation including drawdowns, regime behavior, robustness checks, and overfitting analysis
- Iterate strategies based on failure analysis rather than curve-fitting, identifying where and why strategies break
- Research and engineer custom features and factors beyond standard indicators
- Document strategy logic, assumptions, and failure modes with clarity and precision
- Collaborate with engineers to move research into live trading systems for deployment and monitoring
Requirements
- Hands-on experience developing your own crypto trading strategies with demonstrable results
- Strong quantitative foundation in mathematics, statistics, and probability
- Proficiency in Python including NumPy, Pandas, and vectorized backtesting frameworks
- Experience working with crypto market data across spot, perpetuals, and funding rates
- Deep understanding of market microstructure and crypto-specific trading mechanics
- Ability to explain why a strategy should work theoretically, not just that it backtests well
- Real trading intuition combined with technical execution capabilityno pure theorists or non-coders
Advantages
- Live or paper-traded strategies with documented real performance data
- Experience with derivatives, funding rate dynamics, liquidations, and position management
- Familiarity with event-driven or low-latency backtesting frameworks
- Track record of handling noisy, imperfect data and building robust systems
- Knowledge of regime detection, volatility clustering, or nonlinear market effects
- Prior work at a prop shop, hedge fund, or serious personal trading operation
- GitHub repositories featuring backtesting or trading projects, trading journals, or research notes
- Prior PnL generation (even at small scalehonesty and methodology matter more than size)
Compensation: Competitive base + performance-linked upside. We reward real alpha, not slide decks.
