Quantitative Researcher is responsible for developing, testing, and maintaining systematic and data-driven investment strategies in crypto markets.
The role is focused on research, modeling, and automation, working closely with the Crypto Investment Manager to translate investment ideas into measurable, testable, and scalable strategies.
Key Responsibilities
Quantitative Research & Modeling
- Develop quantitative models for:
- systematic trading strategies
- market-neutral and low-volatility strategies
- yield-enhancement or arbitrage strategies
- Research signals using on-chain and market data
- Design portfolio-level optimization and risk control models
Data & Backtesting
- Build and maintain backtesting frameworks
- Perform historical simulations and stress testing
- Analyze performance metrics (Sharpe, drawdowns, tail risk, liquidity impact)
Execution & Automation
- Work on execution logic and strategy automation
- Optimize trade execution to reduce slippage and costs
- Monitor live strategy behavior and anomalies
Collaboration with Investment & Risk Teams
- Translate high-level investment ideas into quantitative models
- Support risk framework with quantitative metrics and scenario analysis
- Participate in strategy reviews and post-mortems
Requirements
Technical & Mathematical Background
- Strong background in mathematics, statistics, or quantitative finance
- Experience with time series analysis, probability, optimization
- Solid programming skills (Python mandatory; SQL, C++, or Rust is a plus)
Crypto-Specific Knowledge
- Understanding of crypto market microstructure
- Familiarity with DeFi mechanics, AMMs, funding rates, liquidations
- Experience working with on-chain data or APIs is a strong plus
Mindset
- Research-driven and hypothesis-based
- Obsession with data quality and statistical validity
- Comfortable working with uncertainty and noisy signals
- Long-term thinker, not short-term “PnL gambler”
Nice to Have
- Experience in traditional quant funds or prop trading
- Familiarity with ML methods (regression, clustering, basic deep learning)
- Experience deploying strategies in production environments
- Knowledge of risk modeling and capital allocation frameworks