Key Responsibilities:

• Process massive historical tick-level datasets; conduct rigorous feature engineering to identify predictive alpha signals.
• Under the guidance of a mentor, perform statistical modeling on specific market inefficiencies and validate their effectiveness in high-frequency environments.
• Research and implement cutting-edge algorithms from academic literature (Machine Learning, Micro-structure studies) to enhance existing trading models.
• Optimize internal automated tools within the research pipeline to improve the efficiency of strategy iteration and backtesting.

Job Requirements:

• Currently pursuing a Master’s or PhD (exceptional senior undergraduates also considered) in a highly quantitative field such as Mathematics, Physics, Statistics, Computer Science, or Financial Engineering from a top-tier university.
• A rock-solid foundation in probability, statistics, and linear algebra. A deep understanding of stochastic processes or machine learning algorithms is highly valued.
• Expertise in Python (scientific stack: NumPy, Pandas, Scikit-learn, etc.) is required.
• Strong analytical thinking and the ability to extract meaningful insights from complex, noisy, and unstructured datasets.
• Passionate about quantitative trading, meticulous with details, and a proactive communicator who thrives in a collaborative environment.

Preferred Qualifications:

• Top rankings in prestigious competitions such as ACM/ICPC, or high-level Mathematical Modeling contests.
• First-author (or equivalent) publications in premier Machine Learning/AI venues such as NeurIPS, ICML, ICLR, CVPR, or top-tier journals like JMLR, TPAMI.