Systematic Machine Learning Quant Researcher (Deep Learning)

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.