Data Analyst II - Statistics and Data Science - Hybrid Chicago Loop Office

JOB SUMMARY

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.

DEPARTMENT

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 department conducts its own research and leads in designing and implementing rigorous, efficient methods for sampling, weighting, and imputation for sample surveys and evaluation research. It provides expertise and leads NORC strategy on a broad range of methods and techniques, including statistical modeling, machine learning, data linkage, statistical matching, data disclosure control, small area estimation, Bayesian analysis, data quality assessment, data visualization, and developing AI approaches that support NORC’s research. The department collaborates with other departments throughout NORC and leads its own projects.

RESPONSIBILITIES

Collaborate with statisticians, data scientists, technology experts, and subject matter experts to implement and improve data management and analysis activities. Develop programs and scripts for data cleaning, integration, transformation, and harmonization, as well as data quality checks and validation. Support and enhance applications and databases already built for various research needs and produce data documentation and dictionaries. Create data dashboards and visualizations, including preparing charts, graphs, and tables for reports and presentations. Perform various types of data analysis and modeling, often involving multiple data sets. Contribute to documentation, reports, and presentations through writing sections, creating basic analytic summaries, and developing tables and graphics. Apply AI tools to help improve efficiency. Perform other duties as assigned.

REQUIRED SKILLS

Bachelor’s degree in one of the following fields: statistics, math, data science, computer science, or computational social science or related field required. Proficiency with data management, quantitative analysis and modeling, data reduction techniques, and data visualization. At least 2 years’ experience in positions of increasing responsibility, preferably working with large data sets, and conducting statistical and quantitative modeling, statistical/analytic programming, and data mining. Strong foundation in areas of statistics, mathematics, and data science. Coding skills in SAS, R, and Python required. Additional expertise in areas of machine learning such as natural language processing and supervised/unsupervised modeling preferred. #J-18808-Ljbffr NORC at the University of Chicago