We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology.
Requirements
- PhD or PhD candidate in machine learning, computer science, statistics, or a related field
- Experience with sequential modeling and time series forecasting using deep learning
- Experience with deep neural networks and representation learning
- Prior experience working in a data driven research environment
- Experience with translating mathematical models and algorithms into code
- Proficient in programming languages such as Python and R
- Experience with machine learning software libraries such as TensorFlow or PyTorch
- Experience with natural language processing technology a strong plus
- Excellent analytical skills, with strong attention to detail
- Interest in applying machine learning to finance
- Collaborative mindset with strong independent research ability
- Strong written and verbal communication skills
Benefits
- Introduction to industry standard datasets
- Opportunity to implement full breadth of knowledge and training
- Chance to construct own models to solve complex financial problems