Temp Data Analyst

Salary/Position Classification

• $26.50 hourly, Non-Exempt (Eligible for overtime)
• 30 hours per week
• 100% Remote Work Availability:
  No
• Hybrid Work Availability (requires some time on campus):
  No

The Office of DATA compiles and disseminates research on various health topics in North Dakota. As part of these efforts, the group partners with PIs and funders to administer surveys, analyze data, develop data deliverables (fact sheets, policy briefs, infographics, and reports), and disseminate results to stakeholders and diverse audiences.

• Acquire, clean, and prepare data for statistical analysis.
• Analyze data utilizing appropriate statistical techniques using R, Python, or SAS.
• Interpret findings and create written and graphical displays of findings.

• Programming skills are required: SAS, Python and/or R, with the ability to work with tabular health data. Statistical/analytical reasoning is required.
• Excellent interpersonal and presentation skills, with the ability to interface and communicate effectively with team members from diverse backgrounds.
• Excellent planning, time management, organizational, and work coordination skills are needed.

• Demonstrated proficiency in SAS, Python and/or R programming (acquired through coursework, internships, or research projects)
• Statistical training, including coursework or hands‑on experience in statistics, biostatistics, or quantitative methods (e.g., regression, hypothesis testing, study design).
• Ability to handle, clean, and analyze structured tabular data (CSV files, databases, or similar), with strong analytical reasoning and attention to data quality.
• Successful completion of a Criminal History Background Check

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the US and to complete the required employment eligibility verification form upon hire.

This position does not support visa sponsorship for continued employment.

• Experience with electronic health record (EHR) data, epidemiological datasets, or other large‑scale public health data (e.g., BRFSS, NHANES, MIMIC, cohort studies) is highly preferred.
• Familiarity with longitudinal/survival analysis, causal inference methods, or machine learning applied to health data is preferred but not required.

For a successful screening, be sure to review all of the position minimum requirements above and outline how you meet those requirements in your application materials.