- Leverages large volumes of data from multiple university sources into advanced custom predictive and prescriptive models that assess, inform, guide, measure, and monitor the University's strategic and operational initiatives.
- Employs appropriate descriptive, predictive, and prescriptive methods such as statistical modeling, data mining, machine learning, and data visualization to analyze, predict, understand, and improve a variety of institutional outcomes, including but not necessarily limited to academic enrollment, engagement, retention, graduation, and key institutional performance metrics.
- Works with leadership in Strategic Analytics to derive specific insights and recommendations from analytics modeling that can improve institutional decision-making, and to identify opportunities for leveraging University data and data analytics to drive improvement.
- Prepares analytics findings for university constituents outside of Strategic Analytics in a manner that is understandable by personnel who do not have analytics expertise.
- Develops processes and tools to evaluate analytics model performance.
- Identifies needs for additional data and for additional data capturing and analytics infrastructure that can improve organizational efficiency and effectiveness.
- Master’s degree in data analytics, data science, business analytics, operations research, statistics, or a related quantitative research field from a regionally accredited college or university, and at least two years of full-time work experience directly relevant to advanced data analytics, or a Bachelor’s degree in data analytics, data science, business analytics, operations research, statistics, or a related quantitative research field from a regionally accredited college or university, and at least five years of full-time work experience directly relevant to advanced data analytics.
- Demonstrated mastery of SAS, R, SPSS, and/or other relevant predictive and prescriptive modeling software.
- Knowledge of advanced statistical and data mining techniques (e.g., multiple regression, discriminant analysis, logistic regression, Tobit) and their proper usage, and experience with applications.
- Knowledge of a variety of machine learning techniques (e.g., clustering, decision trees, random forests, neural networks) and their real-world advantages/drawbacks.
- Experience with data visualization tools (e.g., Power BI).
- An advanced understanding of quantitative research and mixed methods approaches as well as an ability to manage multiple projects.
- Work experience in a higher education setting.
- Demonstrated understanding of issues in higher education, specifically factors associated with student success.
- Demonstrated understanding of fundamental business principles, including those associated with business process improvement.
- Experience in management of personnel in an organizational environment.
- Demonstrated success in collaborative research projects and/or in working with cross-functional teams.
- Experience working with higher education data is strongly preferred.
- Experience in data querying and data extraction is strongly preferred.
- Knowledge of optimization modeling (e.g., linear / integer programming) techniques.
The University’s annual economic impact on the region is more than $1 billion. The scenic 1,381-acre campus includes a nature preserve, lakes for kayaking and canoeing and miles of hiking trails. Ranked high for quality and value, UNF routinely lands on top national lists by U.S. News & World Report, The Princeton Review, Kiplinger’s, Forbes and others. Small class sizes, individualized attention and transformational learning have become hallmarks of a UNF education.