Quantitative Researcher - Machine Learning

Job Description

Cubist Systematic Strategies, a Point72 affiliate, is seeking a Quantitative Researcher to join their Machine Learning Research group. The researcher will develop computer-driven trading strategies across multiple liquid asset classes. They will use machine learning, deep learning, and natural language processing to build sophisticated trading models.

The role involves managing all aspects of the research process, including data ingestion, processing, analysis, methodology selection, implementation, testing, prototyping, and performance evaluation. The researcher will learn to construct their own models to solve complex financial problems and enhance data prediction capabilities.

Cubist Systematic Strategies offers the opportunity to work with industry-standard datasets and models, actively participating in all stages of research and development.

Responsibilities include:

• Data ingestion and processing
• Data analysis and methodology selection
• Implementation and testing of trading models
• Prototyping and performance evaluation

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
• Proficient in programming languages such as Python and R
• Experience with machine learning software libraries such as TensorFlow or PyTorch
• Excellent analytical skills and attention to detail
• Interest in applying machine learning to finance
• Collaborative mindset with strong independent research ability
• Strong written and verbal communication skills

Cubist Systematic Strategies offers:

• Opportunity to work with industry-standard datasets and models
• Active participation in all stages of research and development