Student Success Analyst, Institutional Research & Academic Analytics

Student Success Analyst, Institutional Research & Academic Analytics

The University of Cincinnati ranks among the nation's best urban public research universities. Home to more than 53,600 students, 12,000 faculty and staff, and over 360,000 living alumni, UC, a Carnegie 1 institution, combines research prowess with a physical setting The New York Times has called "the most ambitious campus design program in the country." UC's momentum has never been stronger as the anchor of the Cincinnati Innovation District, the oldest cooperative education (co-op) program in the country with students earning $94 million annually through paid experiences, an academic health system, and as a member of the Big 12 Conference.

The university contributes $10.6 billion in economic impact to the city and $22.7 billion to the state of Ohio.

The mission of the Office of Institutional Research & Academic Analytics is to collect, coordinate, research, validate, report, analyze, and interpret data about the University of Cincinnati. The office coordinates many of the federal, state, and other mandatory reporting requirements and provides routine and ad-hoc reports and analyses to executive management and across the University to assist in informed decision-making.

The Student Success Analyst will report to the Director of Student Success and Research Analytics, as part of the Academic Analytics team. The key functions of this team are:

• Analyzing trends using historical data, market trends, scholarship modeling, and other relevant bodies of information to create predictive enrollment models and recruitment optimization recommendations.
• Supporting student recruitment and student success research by leveraging analytic expertise and best-in-class products vis data visualization tools such as Tableau and Power BI.
• Providing expertise in using mixed methods (quantitative/qualitative), survey methodology, and online data collection platforms, such as Qualtrics.

Job Overview:
Apply statistical, predictive, and qualitative methods to support data-informed decision-making across student success, enrollment management, and institutional planning. Develop models, analytical tools, and visualizations that surface actionable insights for practitioners and leaders within the Provost Office and across the University. Translate complex analytics into accessible intelligence that supports the full student journey from enrolment to graduation.

Essential Functions:

• Develop and apply statistical and predictive models (e.g., regression, classification, forecasting) to support institutional priorities including student retention, enrolment, and scholarship strategy.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and anomalies in large institutional datasets, with an emphasis on student success outcomes.
• Partner with academic and administrative units to deliver practitioner-facing analytics, presenting findings in formats that are accessible, actionable, and appropriate for non-technical audiences including advisors, program staff, and college leadership.
• Incorporate qualitative methods (e.g., survey analysis, interview synthesis, thematic coding) alongside quantitative approaches to provide richer context for student success and recruitment questions.
• Design, build, and maintain dashboards and data visualizations for internal stakeholders and leadership; publish outputs appropriate for web-based dissemination.
• Collaborate with Digital Technology Solutions (DTS) and data governance teams to access, transform, and validate data needed for analysis.
• Connect data across multiple sources and phases of the student lifecycle — admissions, advising, academic performance, and post-graduation outcomes — to support integrated, evidence-based planning.
• Track and standardize key performance indicators related to student success, enrolment, and recruitment initiatives.
• Employ third-party data resources (e.g., National Student Clearinghouse, ACT, College Board) to contextualize institutional trends and support benchmarking.
• Maintain documentation for models, analytical workflows, and data definitions to support transparency and reproducibility.

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