- Design and complete projects that use advanced statistical and analytic tools to evaluate large educational data sets to address factors that affect STEM education according to the scope, needs, and direction of a five –year funded project
- Collaborate and work in a team environment with the Office of Institutional Research and Planning in collecting and analyzing data
- Maintain compliance with data security, FERPA regulations, submit and revise IRB protocols as necessary
- Meet all reporting, dissemination, and data collection needs required by the project
- Assist in proposal writing and development and securing extramural funding as relevant to the project goals
- Collect, analyze, interpret, and build databases for large complex datasets and create visualizations
- Engage in scholarship of using data and statistical tools to improve retention, success, and diversity of students
- Present and publish research findings at meetings and in peer-reviewed journals
- Implement a prospective database and long-term tracking strategy
- Participate in related activities and research projects as needed
- Participate in other aspects of the NSF S-STEM grant as needed
Commensurate with Education and Experience
A doctorate (foreign equivalent acceptable) in the life sciences, Science Education, Data Science, a quantitative social science, or other closely related disciplines is required. Candidates should have demonstrated skills in verbal and written communication and publication/dissemination of their scholarly work in their field. The candidate should have demonstrated experience in a research setting with analysis, visualization, and interpretation of large datasets, preferably of sensitive data types such as student information or patient data. Candidates must be supportive of the mission of the Land-Grant system. Candidates must also have a commitment to IFAS core values of excellence, diversity, global involvement, and accountability.
Preference given to candidates with experience in science education research (program reviews, learning assessment, retention studies) and programming skills with data science tools (e.g. SAS, R, SPSS or Python). Experience with SQL is a plus.