Temp Data Analyst

• $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.

Duties & Responsibilities

• 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 identityand 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.