Quantitative Researcher - Machine Learning

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