Data Analyst I or II - Methodology and Quantitative Social Sciences - Chicago Loop Hybrid

JOB SUMMARY:  

NORC at the University of Chicago is seeking a qualified Data Analyst I or II (we are open to hiring at either level) to join the Methodology and Quantitative Social Sciences department and support a diverse range of research projects. At NORC, Data Analysts are early career team members who train and work with our Data Scientists and Research Methodologists to perform tasks including importing, cleaning, standardizing, transforming, and validating data sets.

Data Analysts 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 now and in the future. We regret that we are unable to offer visa sponsorship for this position.

Location: This is a hybrid role based in our Chicago Loop office, with a minimum of six days per month in the office.

DEPARTMENT:  Methodology and Quantitative Social Sciences

The Methodology and Quantitative Social Sciences department implements state-of-the-art methodologies 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 Methodological and Quantitative Social Sciences department also conducts its own research and is a leader in designing and implementing rigorous, efficient methods for gathering, evaluating, and analyzing data from primary and secondary sources. The department provides expertise and leads NORC strategy on the use of a broad range of methods and techniques, including research and experimental design, recruitment and retention, instrument design and testing, assessing data quality, evaluating measurement properties of new measures, causal inference methods, machine learning, analysis of clustered data, data visualization, use of novel data sources and technologies to improve data gathering, and building AI solutions that support NORC’s research. The department collaborates with the Statistics and Data Science Department on areas of synergy and intersection and with all NORC subject matter departments, in addition to leading its own projects.

RESPONSIBILITIES:

• Collaborate with methodologists 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 social science with a computational social science emphasis or data science.
• 0-2 years’ relevant experience (internships included.)
• Programming in statistical packages and databases: R, Python, and SQL required.
• Familiarity working with large data sets, conducting statistical and quantitative modeling, melding analytics with strong programming, data mining, clustering and segmentation.
• Strong foundation in areas of social science research, analytic methods, data engineering, machine learning, and scientific methods of inquiry.
• Proficiency with data management, quantitative analysis and modeling, data reduction techniques and data visualization.
• Working knowledge of different types of data that can be sourced whether from administrative data, surveys, social media, or sensor data.
• Strong skills in problem solving and quantitative analysis are required, including a willingness to understand deviations and ask questions about data issues.
• Able to organi