Quantitative Researcher - Machine Learning

<p><strong><u>About Cubist</u></strong></p> <p>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.</p> <p><strong><u>Role/Responsibilities:</u></strong></p> <p>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.</p> <p>Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave.&nbsp; 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.</p> <p>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.</p> <p>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 &amp; 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.&nbsp;</p> <p><strong><u>Requirements: </u></strong></p> <ul> <li>PhD or PhD candidate in machine learning, computer science, statistics, or a related field</li> <li>Experience with sequential modeling and time series forecasting using deep learning</li> <li>Experience with deep neural networks and representation learning</li> <li>Prior experience working in a data driven research environment</li> <li>Experience with translating mathematical models and algorithms into code</li> <li>Proficient in programming languages such as Python and R</li> <li>Experience with machine learning software libraries such as TensorFlow or PyTorch</li> <li>Experience with natural language processing technology a strong plus</li> <li>Excellent analytical skills, with strong attention to