Academic Data Analyst

The Academic Data Analyst position will work closely with the program faculty and staff to formulate and implement the appropriate compilation and analysis of data used for program assessment and self-evaluation. The position also entails assisting with data analysis for program research initiatives.

Responsibilities

• Compile, present, and analyze data associated with course, student or program assessment and evaluation.
• Coordinate compilation and dissemination of assessment and evaluation data in preparation for reports or meetings.
• Organize meetings with appropriate faculty and staff to discuss/present analytics.
• Update and implement improvements to databases.
• Work with faculty and staff to formulate accreditation documents.
• Compile, implement, present, and analyze outcomes data.
• Assure that program assessment and evaluation tools are updated, disseminated, collected and analyzed.
• Assist with the compilation of student grades sheets and facilitate communication of final grades to students, faculty, and Registrar.
• Assist with data preparation for educational research projects.
• Assist with preparation of grant proposals.
• Formulate detailed minutes for meetings related to assessment and evaluation.
• Addressing program emails.
• Assisting with the all aspects related to scheduling admission interview days.

Program Related Responsibilities:

• Assisting with urgent student, faculty, staff, or adjunct needs.
• Assist with copying, faxing, scanning etc.
• Assist with miscellaneous tasks for all administrators in the program.
• Other responsibilities as determined by the program director.

Reports to:

• Directly to Program Director

Qualifications:

• Bachelor's degree required, master's degree preferred.
• Experience in data analysis, assessment tools, or a related field is preferred.
• Must be an advanced user of MS Excel and have extensive experience analyzing quantitative and qualitative data and presenting data graphically.
• Experience using statistical packages for analyzing large datasets (Access, SPSS, etc.) and knowledge of statistics is preferred.