NORC at the University of Chicago is seeking a qualified Data Analyst II to join the Statistics and Data Science department and support a diverse range of research projects. At NORC, Data Analysts are early career team members who train and work with our Statisticians and Data Scientists to perform tasks such as data processing, quality assurance, analysis, and dissemination. For this role, we are looking for someone proficient in SAS, R, and Python.
As part of their data processing responsibilities, Data Analysts import, clean, standardize, transform, and validate data sets. They also research and document data procedures and support the investigation of data problems. They may identify, analyze, and interpret trends or patterns in data sets, support modeling efforts, and help prepare data presentations and reports, including developing charts, graphs, and tables. In addition, the Data Analyst may assist in data collection and harmonization from primary or secondary data sources (e.g., administrative records, commercial data, social media data), preparing data files for delivery, and the maintenance of databases, data systems, and their relevant metadata/dictionaries. The Data Analyst is expected to work collaboratively in a team environment.
Qualified applicants must be eligible to work in the U.S. We regret that we are unable to offer visa sponsorship for this position.
Location: This is a hybrid role based in either our Chicago Loop or downtown DC office, with a minimum of six days per month in the office.
The Statistics and Data Science department implements state-of-the-art statistical methods and develops innovations to deliver reliable data and rigorous analysis to guide critical programmatic, business and policy decisions for NORC clients. The department provides leadership throughout the project lifecycle on study design, data collection, assessment of data quality, quantitative analysis, and dissemination of results. The Statistics and Data Science department also conducts its own research and is a leader in designing and implementing rigorous, efficient methods for sampling, weighting, and imputation for sample surveys and evaluation research. The department provides expertise and leads NORC strategy on the use of a broad range of methods and techniques, including statistical modeling, machine learning methods, data linkage, statistical matching, data disclosure control, small area estimation, Bayesian analysis, assessing data quality, data visualization for analyzing and interpreting data, and developing approaches using artificial intelligence (AI) that support NORC’s research. The department collaborates with the departments throughout NORC, as well as leading its own projects.