Research - Laboratory/Non-Laboratory, Staff/Administrative
Location: Illinois: Chicago
Contributes to the design, implementation, and validation of an efficient and reproducible data processing pipeline.
Builds and rigorously evaluates statistical models using best practices of machine learning and statistical inference.
Prepares project memos, summaries, presentations, reports, and other work products for dissemination targeting policymakers, academic researchers, and other stakeholders, as needed.
Analyzes moderately complex data sets for the purpose of extracting and purposefully using applicable information.
Provides professional support to staff or faculty members in defining the project and applying principals of data science in manipulation, statistical applications, programming, analysis and modeling.
Performs other related work as needed
Master's degree in computer science, statistics, data science, economics or a closely related field.
Technical Knowledge or Skills
Proficiency with statistical data analysis and machine learning using Python or R . The ability to work in both is preferred.
Advanced knowledge of machine learning techniques and algorithms.
Experience developing reproducible and maintainable code.
Excellent written and verbal communication skills, with the ability to present data in a simple and straightforward way for non-technical audiences.
Strong interpersonal skills.
Strong initiative and a resourceful approach to problem solving and learning.
Ability to work independently and as part of a team in a fast-paced environment.
Sound critical thinking skills.
Strong attention to detail with superb analytical and organization skills.
Familiarity with program evaluation and causal inference.
Cover Letter (required)
Reference Information (required)
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Internal Number: JR10663
About University of Chicago (UC)
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