Leading AI quant fund are looking to hire a machine learning quant researcher onto their systematic team.
Role:
- Conduct quantitative research with PM and other AI quants to develop and back-test different machine learning and statistical models, as well as productionize such models.
- Combine sound financial insights and machine learning techniques to explore, analyze, and harness a large variety of datasets.
- Use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave.
- Apply machine learning to a vast array of datasets
- Focuses on Time-Series Transformers Based such as TFT
- Focuses on Pytorch Forecasting, GlutonTS, NeuralForecast Stacks
- Understanding the difference between autoregressive versus latent properties
Requirements:
- Masters/PhD degree in Mathematics, Physics, Financial Engineering, Computer Science, Statistics with specialization in Machine Learning
- Experience working with large datasets and machine learning techniques
- Experience in one or more of deep learning, reinforcement learning, non-convex optimization, Bayesian non-parametrics, NLP or approximate inference.
- Publications at top conferences such as NeurIPS, ICML, ICLR etc. is highly desirable.
- Experience in a high-performance language (ideally C++, or similar languages)
- Outstanding performance in any quantitative field or contest (Kaggle, hackathons, Olympiads, academic contests etc.).
- Experience implementing machine learning algorithms in industry.
- Open to ML quants who are already working within finance or ML quants within tech who are interested to move to finance.
- Trading Background is not a pre-requisite for the above role.
