We are a leading AI-driven quantitative fund seeking to strengthen our systematic team with a Data Science Focused Quant Researcher. This role involves leveraging advanced data science and machine learning capabilities to innovate and refine trading strategies within the financial markets.
Role Responsibilities:
- Collaborate with Portfolio Managers and other AI specialists to conduct robust quantitative research. This includes developing, testing, and deploying various machine learning and statistical models.
- Employ sophisticated data science techniques to extract actionable insights from vast and diverse datasets, integrating these insights into financial models.
- Utilize scientific methods to create advanced trading models, contributing to predictive analytics regarding market movements.
- Implement machine learning algorithms across extensive datasets to enhance model effectiveness and financial strategies.
Requirements:
- Advanced degree (Master’s/PhD) in Mathematics, Physics, Financial Engineering, Computer Science, or Statistics, with a strong focus on Machine Learning.
- Proven experience handling large datasets and applying machine learning techniques to real-world problems.
- Expertise in areas such as deep learning, reinforcement learning, non-convex optimization, Bayesian non-parametrics, natural language processing, or approximate inference.
- Publications in top-tier conferences like NeurIPS, ICML, ICLR, etc., are highly preferred.
- Proficiency in high-performance programming languages, ideally C++ or similar.
- Demonstrated excellence in quantitative analysis or competitions (e.g., Kaggle, hackathons, mathematical olympiads).
- Practical experience in implementing machine learning algorithms in a professional setting.
- Open to candidates transitioning from tech to finance, or current finance professionals with a strong machine learning background.
