Key Responsibilities and Required Skills for University Research Analyst

🎯 Role Definition

The University Research Analyst is responsible for designing and executing research and evaluation projects across academic departments and administrative units. This role combines robust quantitative and qualitative methods, data governance, and stakeholder engagement to produce reproducible analyses, dashboards, and written deliverables that support grant development, program improvement, institutional research, and scholarly publications. The Analyst works closely with principal investigators, faculty, departmental managers, and external partners to ensure high-quality, compliant research outputs.

📈 Career Progression

Typical Career Path

Entry Point From:

Research Assistant or Research Coordinator in a university or nonprofit
Data Analyst, Institutional Research Assistant, or Program Evaluation Assistant
Graduate research assistantships (MA/MS or PhD trainees)

Advancement To:

Senior Research Analyst / Senior Data Analyst
Institutional Research Manager or Director of Institutional Research
Research Operations or Grants Manager; Principal Investigator on funded projects

Lateral Moves:

Program Evaluation Specialist
Policy Analyst or Data Scientist within higher education
Grants & Compliance Analyst

Core Responsibilities

Primary Functions

Design and implement quantitative and qualitative research studies, including defining research questions, choosing appropriate methodologies, and developing data collection plans aligned with faculty and departmental objectives.
Conduct sophisticated statistical analyses using R, Python, Stata, SPSS, or SAS, applying regression, multilevel modeling, time series, survival analysis, or other advanced techniques as project needs dictate.
Develop and program online surveys and assessments (Qualtrics, REDCap, SurveyMonkey), manage sampling strategies, and implement protocols to maximize response rates and data quality.
Clean, transform, and merge complex administrative, longitudinal, and experimental datasets using reproducible workflows, data dictionaries, and version control (Git) to ensure data integrity and provenance.
Build and maintain interactive dashboards and visualizations in Tableau, Power BI, or custom web apps (Shiny, Dash) to communicate real-time metrics and research findings to faculty, leadership, and funders.
Prepare clear, publication-quality written reports, executive summaries, and slide decks that translate technical results into actionable recommendations for diverse stakeholders.
Support grant development by producing data-driven sections (methods, evaluation plans, preliminary analyses), assembling institutional data, and estimating sample sizes and power calculations.
Manage Institutional Review Board (IRB) submissions, amendments, and adverse event reporting; ensure research protocols meet ethical standards and data privacy regulations (FERPA, HIPAA where applicable).
Coordinate multi-site or multi-department research collaborations, including data-sharing agreements, Memoranda of Understanding (MOU), and timelines for deliverables across internal and external partners.
Perform literature reviews and evidence syntheses to contextualize study designs, justify analytic choices, and support grant narratives and manuscript introductions.
Lead program evaluation activities using frameworks such as logic models, theory of change, formative and summative evaluation techniques, and create measures for outcomes and impacts.
Design experiments and quasi-experiments (randomized controlled trials, matched designs, difference-in-differences) and oversee implementation fidelity and treatment integrity checks.
Develop and validate measurement instruments, codebooks, and scales; conduct reliability and validity testing and document psychometric properties for reporting and reuse.
Automate routine data processes and ETL pipelines to reduce manual work, using SQL, Python scripts, scheduled jobs, and cloud resources where available to ensure scalable reproducible analyses.
Conduct data quality assurance and cleaning routines including missing data strategies, outlier detection, and imputation, and document all procedures to support reproducibility and audit trails.
Mentoring and supervising undergraduate and graduate student research assistants, delegating tasks, reviewing code and analyses, and providing training in research methods and best practices.
Create and run predictive models and machine learning pipelines for student success, enrollment forecasting, faculty productivity metrics, or program risk assessment, with careful attention to fairness and interpretability.
Track and manage project budgets, procurement for research tools, and reporting requirements for sponsored awards, coordinating with sponsored programs and grants administration offices.
Present research findings at departmental meetings, faculty seminars, conferences, and external stakeholder briefings; prepare materials for peer-reviewed publication and institutional dissemination.
Implement data governance and security procedures, maintain data inventories and access logs, and work with IT and legal teams to ensure compliance with institutional policies and data-sharing agreements.
Facilitate workshops and training sessions for faculty and staff on data analysis, interpretation of results, data visualization best practices, and use of research tools and platforms.
Collaborate with IT, institutional research, and other units to integrate administrative databases, student information systems, and learning management system data for enriched analytics.
Monitor project timelines, create project plans with milestones, and use Agile or other project-management methodologies to ensure timely delivery of research outputs.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Assist in preparing compliance documentation for audits and sponsor reports.
Maintain and curate institutional data catalogs and metadata repositories.
Participate in community of practice meetings, promoting reproducible research standards across campus.
Provide support for public-facing research communication such as blog posts, press releases, and social media summaries.

Required Skills & Competencies

Hard Skills (Technical)

Advanced statistical analysis: regression, multilevel models, causal inference, survival analysis, and power analysis.
Proficient in one or more statistical programming languages: R and/or Python; familiarity with Stata, SAS, or SPSS is a plus.
Strong SQL skills for querying relational databases and building ETL processes.
Data visualization and dashboarding: Tableau, Power BI, R Shiny, Plotly, or equivalent.
Experience with survey design and implementation tools such as Qualtrics, REDCap, or SurveyMonkey.
Knowledge of research ethics and human subjects protection; experience preparing IRB applications and managing compliance.
Version control and reproducible workflows: Git, RMarkdown, Jupyter Notebooks, containerization basics (Docker) for reproducible analyses.
Familiarity with machine learning libraries and predictive modeling (scikit-learn, caret, tidymodels) and understanding of model evaluation and fairness considerations.
Advanced Excel skills, including pivot tables, Power Query, and VBA/basic automation.
Experience with institutional data systems and administrative student datasets (ERP, SIS, LMS) and integration approaches.
Experience preparing grant deliverables, evaluation plans, and supporting sponsored projects reporting.
Familiarity with data governance, privacy regulations (FERPA, HIPAA), and data-sharing agreements.

Soft Skills

Excellent written communication for reports, manuscripts, grant narratives, and executive summaries.
Strong verbal communication and presentation skills for diverse academic and administrative audiences.
Critical thinking and problem-solving with attention to detail and commitment to reproducible, transparent methods.
Stakeholder management and collaboration skills; ability to work with faculty, administrators, IT, and external partners.
Project management and organizational skills to manage multiple concurrent studies with competing deadlines.
Teaching and mentoring aptitude to train students and staff in research methods and tools.
Adaptability and eagerness to learn new tools, statistical methods, and domain knowledge.
Ethical judgment and integrity in handling sensitive student and research data.
Time management and prioritization skills to balance short-term requests and long-term research goals.
Customer-service orientation when responding to ad hoc requests from campus units and external collaborators.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Statistics, Data Science, Economics, Public Policy, Education, Sociology, Psychology, Biostatistics, or related field.

Preferred Education:

Master's degree or higher (MS, MPP, MEd, MPH, or PhD) in a quantitative discipline, evaluation, public policy, biostatistics, or related research field.

Relevant Fields of Study:

Statistics, Data Science, Applied Mathematics
Education Research, Sociology, Psychology
Public Policy, Economics, Biostatistics, Epidemiology
Evaluation & Measurement, Research Methodology

Experience Requirements

Typical Experience Range:

2–5 years of relevant experience in applied research, institutional research, program evaluation, or data analysis in higher education, nonprofit, or research settings.

Preferred:

3–7+ years of experience supporting sponsored research, grant-funded evaluations, or institutional research initiatives; prior experience with IRB processes, grant reporting, and cross-functional academic collaboration is highly desirable.