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Senior Assessment and Analytics Scientist, Division of Campus Life

Wake Forest University
Winston-Salem, United Statesfull_timeVerifiedPosted 26 Jul 2025

About the role

External Applicants: 

Please ensure all required documents are ready to upload before beginning your application, including your resume, cover letter, and any additional materials specified in the job description.

Cover Letter and Supporting Documents:

  • Navigate to the "My Experience" application page.

  • Locate the "Resume/CV" document upload section at the bottom of the page.

  • Use the "Select Files" button to upload your cover letter, resume, and any other required supporting documents. You can select multiple files.

Important Note: The "My Experience" page is the only opportunity to attach your cover letter, resume, and supporting documents. You will not be able to modify your application or add attachments after submission.

Current Employees:

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A cover letter is required for all positions; optional for facilities, campus services, and hospitality roles unless otherwise specified.

Job Description Summary

The Senior Assessment and Analytics Scientist provides leadership for effectively and collaboratively using high-impact, data-driven approaches related to continuous improvement for the Division of Campus Life. These activities will facilitate the achievement of continual learning improvement; inform new projects in student success; provide more formative assessment support for programs and program review; and provide consultative support for administrative effectiveness as needed.

Job Description

Essential Functions: 

  • Develop and lead collaborative measurement, evaluation, and analysis efforts related to student success.

  • Provide support, tools, and training for leaders to comply with university standards, accreditation requirements, and other compliance frameworks.

  • Create tools, training, and other technical resources to help leaders and their teams integrate measurement, evaluation, and analysis into evidence-informed practices for their compliance, program development, and other activities.

  • Serve as a central technical resource on and implement projects in:

    • Instrument development and psychometric methods.

    • Advanced qualitative, quantitative, and mixed-method evaluation and research methods.

    • Assessment and analysis strategies.

    • Formative, summative, and other approaches to program evaluation and assessment planning.

    • Evidence-informed practice.

    • Large data management and analysis.

  • Create accessible and actionable reports and visualizations to inform data-driven decision-making.

  • Develop and implement strategies that build and sustain a culture of assessment, evaluation, inquiry, and other components of evidence-informed practice.

  • Collaborate with other units across the University to ensure the most effective, and efficient practices in measurement, evaluation, analysis, and information management, and other areas as relevant.

  • Lead the identification, implementation, and management of software, information management, and other tools related to measurement, evaluation, and analysis.

  • Serve on relevant university committees.

  • This position is eligible for flexible work.

Required Education, Knowledge, Skills, Abilities:

  • Masters Degree in assessment and measurement; higher education; educational research; social sciences research; statistics; or related field.

  • Five or more years post-degree experience in a higher education setting leading qualitative, quantitative, and mixed-methods research; multivariate statistical analysis and data visualization; and assessment and program evaluation efforts.

  • Experience working collaboratively with a wide range of constituencies, including students, staff, faculty, and administrators.

  • Experience leading the technical and logistical components of multidisciplinary research projects in collaboration with experts from multiple substantive backgrounds.

  • Experience with advanced multivariate statistical methods, such as latent variable modeling, causal analyses, predictive analytics, longitudinal and time series modeling, and psychome

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Company

Wake Forest University

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