Machine Learning Researcher
This role focuses on advancing predictive financial models using deep learning to extract alpha factors from complex market data, with a strong emphasis on strategy ensemble and continuous model refinement. The ideal candidate will bridge cutting-edge research with practical implementation to enhance portfolio performance and robustness.
Skills required
About this role
Key Responsibilities：
1. Mine alpha factors and build predictive models via deep learning based on multi-dimensional financial market data.
2. Explore signal fusion and strategy ensemble approaches to enhance model robustness and portfolio return characteristics.
3. Rapidly prototype, reproduce and optimize state-of-the-art deep learning models with mainstream ML frameworks.
4. Stay updated on latest academic and industrial research, conduct ongoing model iteration and performance enhancement.
Qualifications：
1. Bachelor’s degree or above from top domestic and international universities, majoring in Computer Science, Mathematics, Statistics, Machine Learning or related quantitative disciplines.
2. Strong theoretical foundation in machine learning, proficient in Python and mainstream deep learning frameworks; capable of end-to-end data processing and independent modeling.
3. Hands-on research or project experience in time series forecasting, NLP or other deep learning related domains.
4. Logical, rigorous mindset with excellent self-learning capability and strong interest in applying ML to quantitative finance.
5. Prior internship or working experience in Internet, AI, fintech or quantitative domains.
Preferred Qualifications：
1. Kaggle competition awards or first-author publications at top ML conferences (NeurIPS / ICML / ICLR).
2. Relevant internship experience in quantitative trading, asset management or financial technology.
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