Quantitative Researcher - Machine Learning

Canvas Fundamental Research Group is seeking a quantitative researcher for their Machine Learning Research group. The role involves developing sophisticated trading models using rigorous scientific methods and participating in all stages of research and development of financial models.

What you'll do:

• 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
• Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave
• Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation
• Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features
• Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning
• Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry

What they're looking for:

• 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
• 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
• Experience with natural language processing technology a strong plus

Benefits:

• Comprehensive benefits package