Key Responsibilities

• 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).