By the end of 2025, there will be 55 billion-dollar quantitative private equity firms in the country, of which 47 will be our customers; there will also be more than 100 quantitative private equity firms with 0 to 100 billion in assets, 15 (non-asset management) quantitative self-managed funds, and a small number of foreign quantitative hedge funds, all of which will closely cooperate with our company.
[We will not charge you any fees!]
Please note:
1. This position does not require immediate employment, and we welcome inquiries at any time!
3. If you are currently working in the same industry or have already received an offer from a certain company, I will recommend anonymously from your current company and hide your name, and supplement appropriate privacy protection measures until you initiate an interview or make significant progress.
[The following is a formal introduction]
We provide you with more than just a job, but also a chance to fight side by side with top-notch teams:
- Stable work and life balance (usually 965), and leave plenty of free space within working hours for you to deepen your learning, explore your interests, focus your thoughts or relax and rejuvenate.
- With a pleasant rest area and self-run coffee shop, drinks and snacks are supplied around the clock; equipped with an employee fitness center, high-end ergonomic chairs and high-definition multi-screen workstations, we create an ideal office environment in every way.
- The team is composed of elite from renowned universities and industry experts. Rich research directions are available for selection, encouraging personalized development; technical sharing is held regularly every week, with an open atmosphere and harmonious collaboration.
- In addition to employee funds, zero-cost dividends, partner plans, quarterly special contributions, and generous year-end bonuses, we place greater emphasis on sharing every growth joy with you.
Responsibilities
- Responsible for the full process of model development, testing and deployment of quantitative strategies. The core work includes:
- Apply statistical learning and ensemble learning methods for multi-dimensional factor development and return prediction.
- Or design deep neural networks (such as Transformer, GNN) to process high-dimensional, low-frequency/high-frequency sequence data for pattern recognition.
- Or build reinforcement learning (including offline/hierarchical RL) agents, develop combinatorial optimization, order execution strategies.
- Lead the full model process: from feature engineering, backtesting framework development, overfitting control to low-latency deployment in production environments.
Technical requirements
- At least master one of the following directions in model development:
- Traditional machine learning: ensemble learning, probabilistic graphical models, feature engineering, and interpretability.
- Deep learning: CNN/RNN/Transformer/GNN, and its application in sequential and graph-structured data.
- Reinforcement learning: based on model-based/model-free methods, simulation environment construction, risk-sensitive strategy optimization.
- Have solid experience in model evaluation, out-of-sample testing, and real-time optimization. Familiar with model compression and deployment.
- Able to closely collaborate with signal research, trading system team, complete the technical implementation and system integration of the strategy.
