Data Scientist

5 days ago

Singapur, Singapore Point72 Full time

Overview We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance. 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. Responsibilities Identification of new data sets Engaging with vendors to understand characteristics of datasets Building processes and technology tools to ingest, tag and clean datasets Analysis of datasets to generate descriptive statistics and propose potential applications of data Research of potential "alpha signals" for presentation to Portfolio Managers Monitoring and enhancing the automated data collection and cleansing infrastructure Research on new technologies for improved data management and efficient retrieval Qualifications Ph.D. in computer science, mathematics, physics, statistics or another discipline involving rigorous quantitative analysis techniques At least 1 year of experience as a Data Scientist, quantitative researcher or in a similar role Experience working with large data sets, including classification, regression, distribution analysis, and predictive modeling Experience applying statistical tests to large data sets Programming skills in SQL, T-SQL, SQL Server or PL-SQL Programming skills in Python and at least one of C#, C++, or Java Financial industry experience preferred but not required Experience dealing with intraday, tick and order book data a plus Strong problem solving skills Intellectual curiosity and a love of learning Attention to detail and a love of process Strong oral and written communication skills Seniority level Entry level Employment type Full-time Job function Engineering and Information Technology #J-18808-Ljbffr

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