Research, design, and implement quantitative trading and liquidity models that enhance execution quality and market performance.
Apply advanced statistical and machine learning techniques to extract short- to medium-term predictive signals from high-frequency and on-chain data.
Conduct rigorous backtesting, parameter optimization, and stress testing to validate strategy robustness.
Analyze market microstructure across multiple venues to identify patterns and inefficiencies in liquidity and pricing.
Build and maintain simulation frameworks and research infrastructure in Java, Python and C++ to support continuous model improvement.
Collaborate closely with developers and trading teams to deploy models and monitor live trading performance.
Continuously explore new data sources, DeFi protocols, and liquidity mechanisms to drive alpha generation.
Qualifications
Degree in a quantitative discipline such as Mathematics, Physics, Computer Science, Engineering, or Statistics (advanced degree preferred but not required).
5+ years of experience in quantitative research, trading, or systematic strategy development within crypto, HFT, or traditional financial markets.
Strong programming skills in Java and Python proficiency in C++ or Rust is a plus.
Deep understanding of market microstructure, statistical modeling, and time-series analysis.
Proven ability to work with large, noisy datasets and apply advanced data analysis techniques.
Strong problem-solving skills, creativity, and intellectual curiosity.
Entrepreneurial mindset - comfortable with autonomy, ambiguity, and rapid iteration.
Preferred
Hands-on experience in liquidity or execution strategy design (CeFi or DeFi).
Knowledge of exchange APIs, order types, and real-time data systems.
Experience in developing machine learning models and automated research pipelines.
Track record in competitive quantitative challenges (e.g., Kaggle, ICPC, math competitions).
