Statistician III

Job no: 503944 Work type: Regular Full-Time Location: Washington, DC Capability Area: Statistics and Data Science

NORC at the University of Chicago is seeking a qualified Statistician III to join the Statistics and Data Science department. Statisticians in this role work cross-functionally across a diverse portfolio of projects in NORC’s substantive areas - health, society, economics, and global research - to generate trustworthy data and analytic insights. They apply mathematical statistics and survey methodology, including sampling, weighting, and variance estimation.

Statisticiansdevelop and apply robust solutions and reproducible workflows across the data lifecycle for data cleaning, integration of multiple data sources, transformation, harmonization, validation, and analysis. NORC statisticians also support the responsible release of data and findings by applying techniques to protect study participant confidentiality, such as statistical disclosure limitation and synthetic data. They design and develop dashboards and data visualizations that communicate effectively to a variety of audiences.

The Statistician III also leverages machine learning and AI to enhance analytic efficiency and impact.

Additional responsibilities include mentoring early-career staff, presenting results to clients and professional audiences, contributing to proposals and business development efforts, and supporting the delivery of high-quality technical products. Statisticians III are expected to work collaboratively in a team-oriented 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
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This is a hybrid role based in either our Chicago Loop or downtown Washington, DC office, with a minimum of six days per month in the office. Remote work status may be considered for outstanding candidates.

The Statistics and Data Science department implements state-of-the-art statistical methods and develops innovations to deliver reliable data and rigorous analyses that 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, statistical disclosure limitation, 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 departments throughout NORC, as well as leading its own projects.

Provide statistical expertise across projects, including study design and advanced methods; contribute to technical planning and help manage quality of work products from other staff.

Lead survey statistics tasks, including sample selection, weighting, nonresponse analyses, variance estimation; contribute to sample design and analysis sections of reports.

Develop robust data engineering and analytics pipelines to support reproducible, scalable analysis and ML/AI applications.

Uphold data disclosure limitation and data privacy best practices; apply statistical disclosure limitation for public releases and restricted-use data.

Create dashboards and data visualizations; design effective, stakeholder-ready visual products and set data visualization standards for projects.

Design and develop programs/scripts for data cleaning, integration, transformation, harmonization, and validation; create and maintain data documentation and dictionaries.

Write and implement SAS, R, and Python…