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Python, generate visualizations using Tableau, and adhere to all administrative policies and procedures. Prior to delivery of data, the incumbent will assist with data quality checks, making sure that
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. Responsibilities Work with nonprofit and social impact partners to design and execute data science projects for social good across multiple verticals including climate, energy, food, agriculture, and human rights
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programming and instrumentation interface skills in Python and C programming languages. Knowledge of Linux-based operating systems is highly desirable. Strong computer skills including calendaring, document
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. Programming skills using Python (preferred), Golang/Javascript (a plus), or other languages like C/C++, Java, or C# with advanced skills in at least one of these languages. Knowledge of web applications, REST
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, statistical applications, programming, analysis and modeling. The Ovarian Cancer Research Lab at the University of Chicago is seeking a full-time, on-site Clinical Data Scientist/Analyst to support multiple
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of research, including data collection, model development, and implementation. This position requires an individual who is able to work as part of small research teams, and on multiple projects concurrently
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with petabyte-scale in multiple computing environments. Trains research assistants in the use of new and existing computational tools. Acts as a point of contact for code management and data storage
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to improve software quality, testability, and fault tolerance. Manage multiple projects simultaneously, supporting faculty and postdoctoral researchers in achieving their research objectives. Collaborate with
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conversational contexts. Our lab conducts observational and experiment-based studies of language behavior in adults and children across multiple cultural and linguistic settings. The use of open source tools
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: Knowledge of machine learning techniques in data analysis. Familiarity with statistical analysis packages such as Stata, R, or Python. Advanced knowledge of research techniques and methods in relevant