Internship Duration
- Minimum of 8 Weeks
Responsibilities
- Signal Mining: Deeply analyze extensive high-frequency market data to construct, select, and evaluate alpha factors with predictive power.
- Model Iteration: Leverage machine learning and statistical modeling to build and train quantitative models, continuously enhancing predictive accuracy and stability.
- Strategy Conversion: Utilize mathematical tools to efficiently turn model signals into trading returns, and participate in live strategy performance attribution.
- Infrastructure Collaboration: Collaborate with the team to design and develop efficient research frameworks.
Requirements
- Academic Background: Master's or Ph.D. students graduating in 2026/2027 in Applied Mathematics, Statistics, Computer Science, Financial Engineering, or related fields (exceptional undergraduate candidates may be considered with relaxed criteria).
- Programming Skills: Strong proficiency in Python and familiarity with the scientific computing ecosystem (e.g., NumPy, SciPy, Pandas, Scikit-learn, Numba, Cython, etc).
- Professional Qualities: Strong engineering capabilities and coding standards, with the ability to effortlessly navigate data science tools.
- Personal Traits: A deep curiosity for technology, strong self-drive, exceptional communication skills, and a collaborative mindset.
- Language Skills: Excellent English reading and writing abilities, capable of seamlessly navigating technical documentation.
Bonus Points
- Prior internship experience at leading quantitative financial institutions.
- Significant research or practical experience with deep learning models.
- Award-winning experience in national or international academic competitions (e.g., high rankings in IMO/CMO, ACM, or Kaggle, etc).
- Mastery of distributed computing (e.g., Spark, Ray, Dask, etc.) or publications of high-quality papers in core journals or conferences.