Data Analyst - Community Research, Evaluation and Development (Extended Temporary)

The Community Research, Evaluation and Development (CRED) team of the Norton School of Human Ecology is seeking a highly skilled Data Analyst to support ongoing research and evaluation projects in the social sciences, often focused on health and well-being and working with community-based partners. The CRED team (https://norton.arizona.edu/cred) conducts high-quality, culturally responsive, community-based research and evaluation that promotes the health and well-being of families and individuals throughout the Southwest. For more than a decade, CRED has been committed to working on the ground with partners across Arizona, including Cooperative Extension, tribes, state and local-governments, justice systems, school and non-profit agencies. Our primary focus is to generate accessible, actionable information that CRED’s community partners can use to support their programs and policy making decision-making.

The successful candidate for this position will work collaboratively with other CRED team members and focus on quantitative data analysis and creation of interactive data visualizations. This position requires strong data management and analytical skills to wrangle data from multiple sources, including large public datasets such as the decennial Census and American Community Survey as well as administrative datasets from state and local agencies, to create products that support data-informed decision-making. Activities will include acquiring, managing, and analyzing small and large datasets; designing, executing, and interpreting statistical analyses; creating static and interactive data visualizations; and consulting on research and evaluation study designs with both CRED team members and community partners with a broad range of research experience. This candidate will have excellent planning and communications skills, self-direction, and a commitment to community-oriented research.

This is an extended temporary position. Positions on the CRED team are funded via grants and contracts. This position currently has funding through end of state fiscal year (June 30) 2024. The team is currently pursuing grant and contract opportunities that would extend the funding for this position.

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• Develops data dashboards and other interactive data reporting products.
• Conducts appropriate statistical analyses of large and small datasets, including both administrative and survey, and collaborates with CRED team members on data interpretation and visualization.
• Prepares periodic reports, such as progress, status, results, or other regular and special reports.
• Develops and maintains databases, and creates data acquisition and management plans for ongoing research and evaluation projects.
• Provides technical assistance to CRED team staff and partners in accessing data from various databases and systems, and may supervise research assistants.

Knowledge, Skills & Abilities:

• Knowledge of social science research methods.
• Knowledge of data management best practices and database structures.
• Proficiency with Excel, R, and other statistical software.
• Excellent planning skills (to be able to design and execute multiple simultaneous projects).
• Excellent organizational skills and attention to detail.
• Effective communication skills (to be able to explain and justify analytical decisions and document analyses in code and formal reports).
• Ability to learn and provide support to colleagues for new data internal systems as necessary.
• Interest in one or more of the following: community development, economic mobility, health and/or social programs, education, food systems, and/or justice systems

• Masters degree.
• Minimum of 3 years of relevant experience required.

• Experience collaborating with social science researchers.
• Experience in grant writing, needs assessment, process evaluation, and outcomes research.
• Experience using R for data management and analysis including data wrangling skills, for example, proficiency in tidyverse, lubridate, or other data wrangling packages, and publishing skills, such as Shiny and knitr.
• Experience with design and use of data structures or databases (e.g. REDCap, Access).
• Experience with reproducible programming methods (markdown or Jupyter notebooks) in R or python.
• Experience with interpreting scripts/output from other statistical packages such as SAS, Stata, and SPSS.
• Experience analyzing geospatial data and creating both interactive and flat mapping products.