Cubist Systematic Strategies is looking for a quantitative researcher to develop sophisticated trading models and enhance data prediction capabilities within the financial services industry by applying machine learning to finance.
Requirements
- Experience with sequential modeling and time series forecasting using deep learning
- Experience with deep neural networks and representation learning
- 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
Responsibilities
- use a rigorous scientific method to develop sophisticated trading models
- manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation.
- learn how to construct their own models in order to solve complex financial problems
- enhance data prediction capabilities within the financial services industry.
- 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.
- incorporate the data into innovative functional models
- construct and develop features from raw data
Other
- PhD or PhD candidate in machine learning, computer science, statistics, or a related field
- Prior experience working in a data driven research environment
- Interest in applying machine learning to finance
- Collaborative mindset with strong independent research ability
- Strong written and verbal communication skills
