Ascension LLC is seeking a Senior Statistician / Quantitative Evaluation Analyst to support a U.S. Department of Education evaluation focused on the next cycle of Regional Educational Laboratories and Comprehensive Centers. This role is designed for a highly experienced quantitative analyst who can develop and execute rigorous statistical analysis strategies, analyze extant performance data, support repeated data collection, produce reproducible statistical code, and contribute to high-quality technical reports suitable for IES review and potential public release.

The ideal candidate brings advanced statistical analysis experience in education, public policy, human services, program evaluation, or federally funded technical assistance programs. This position is critical to helping the evaluation team assess prior-cycle outcomes, analyze Annual Performance Reports, Annual Evaluation Reports, stakeholder survey data, and other extant datasets, and support future-cycle repeated data collection and summative analysis. The Department anticipates that this evaluation may use primary data collection, statistical analysis of extant data, or both, and that findings will be used for timely program improvement.

This is not an executive management role. It is a senior technical role requiring strong independent judgment, statistical rigor, reproducible analysis practices, and the ability to translate complex quantitative findings into clear, defensible conclusions for technical reports, derivative products, and stakeholder-facing briefings.

Summary of the Contractor Role

The Senior Statistician / Quantitative Evaluation Analyst will support Tasks 2, 3, 4, and 8 of the anticipated evaluation effort, including prior-cycle outcomes analysis, repeated data collection and summative analysis, technical reporting, and preparation of data files and statistical code. The analyst will work closely with the Project Director, Principal Evaluation Lead, qualitative researchers, data collection staff, and technical report writers to ensure the evaluation design, analysis methods, statistical assumptions, and resulting findings are methodologically sound and clearly documented.

The role requires a detail-oriented and self-driven professional who can work in an ambiguous federal evaluation environment, identify analytic limitations, recommend practical solutions, and maintain the documentation needed for peer review, reproducibility, and potential restricted-use data release. The contractor should be comfortable analyzing administrative and survey datasets, developing analysis plans, supporting sampling and power considerations where applicable, and advising on disclosure avoidance planning for data files that may contain identifying information.

The Department expects technical reports to include a clear description of research questions, evaluation purpose, methods, analysis, and findings. These reports must be of high technical quality and suitable for review in a top-tier peer-reviewed journal or equivalent. The analyst will therefore play a central role in ensuring that statistical analyses are accurate, transparent, reproducible, and aligned with IES standards.

Anticipated Day-to-Day Activities

The Senior Statistician / Quantitative Evaluation Analyst will be expected to:

• Develop statistical analysis strategies aligned to the evaluation’s research questions, available data sources, and reporting timelines.
• Review extant performance data, including APRs, AERs, stakeholder feedback surveys, and related administrative datasets, to determine analytic usability, completeness, and limitations.
• Assess prior-cycle REL and Comprehensive Center outcomes using available quantitative indicators and defensible statistical methods.
• Support repeated data collection planning for Part 2 of the evaluation, including recommendations related to survey design, sampling, response rate assumptions, subgroup analysis, and measurement consistency over time.
• Analyze quantitative survey, administrative, and performance measurement data to identify patterns, trends, differences over time, and evidence of program model implementation.
• Conduct descriptive, inferential, longitudinal, or quasi-experimental analyses as appropriate to the available data and approved evaluation design.
• Prepare tables, figures, and statistical summaries that clearly communicate findings for technical reports and non-technical derivative products.
• Document analytic decisions, data transformations, variable construction, assumptions, limitations, and quality checks in a clear and reproducible manner.
• Produce reproducible statistical code using approved software such as R, Stata, SAS, Python, or equivalent tools, depending on project and customer requirements.
• Perform quality assurance checks on statistical outputs, syntax, tables, figures, and report language to ensure accuracy and consistency.
• Support development of the Part 2 analysis plan, including recommended measures, analytic models, data sources, timing, and anticipated limitations.
• Advise the evaluation team on sampling, weighting, missing data, nonresponse, measurement error, and statistical power considerations where applicable.
• Contribute technical language for methods sections, appendices, analysis plans, data documentation, and peer review responses.
• Support preparation of de-identified data files and statistical code for government delivery at project closeout.
• Coordinate with the team on disclosure avoidance planning to reduce the risk of inadvertent disclosure in public-use or restricted-use files.
• Maintain compliance with IES data handling requirements, including secure-server use and restrictions on the use of IES data for machine learning or AI model training. The draft SOO requires non-public IES data to be stored and analyzed on IES’s secure server and prohibits use of IES data, inputs, or outputs to train, fine-tune, improve, or update AI systems.
• Participate in project meetings, technical working group preparation, milestone briefings, and internal quality reviews as needed.