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Quantitative Software Developer

Point72

San Francisco, CAOn-siteFull-time

About this role

Job Application for Quantitative Software Developer at Point72 Quantitative Software Developer New York Apply Role/Responsibilities: We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance. Cubist’s data services group is looking for a junior Quantitative Software Developer to join our dedicated team. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers in the world. This exceptional individual will be a member of a small team of data and software developers who play a vital role in ensuring the smooth day-to-day implementation of a large research infrastructure, and the live production trading of billions of dollars of capital across global capital markets, including equities, futures, options and other financial instruments. Responsibilities include: - Building processes and technology tools to ingest, tag and clean datasets. - Assisting Data Scientists with data processing, enrichment, and product development. - Contributing to existing infrastructure and applications primarily written in Python, but also Go and C++. - Monitoring and enhancing the automated data collection and cleansing infrastructure. - Researching new technologies for improved data management and efficient retrieval. Requirements: - B.S. or higher in computer science, engineering or a relevant quantitative field such as Mathematics, Statistics, Physics. - Proficiency in Python and its ecosystem (numpy, pandas, polars, scikit-learn), with an understanding of Python and library internals - Proficiency with Go and/or C++ is not required but is a big plus - Hands-on experience with software architecture and engineering best practices (testing, CI/CD, monitoring, profiling, version control) - Strong problem-solving skills, ability to analyze and solve intricate problems, optimize co

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