About Cubist

Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.

Role/Responsibilities

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.

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

The annual base salary range for this role is $200,000-$300,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

Quantitative Researcher - Machine Learning

Job Type