Data Analyst - Physical Therapy & Rehabilitation Science

Required Qualifications

• A degree in engineering and a master’s degree in data science/analytics or equivalent combination of education and experience is required.
• Must be proficient in hardware, software, and data analytical techniques as related to educational metrics.
• Excellent capacity to communicate both verbally and in writing the complex algorithm processes to enhance understanding among academic leaders nationwide.
• Demonstrated understanding of educational metrics as they relate to learner preparedness, career regret, exhaustion, empathy, tolerance for uncertainty, and didactic competencies.
• Demonstrated capacity to analyze complex relationships among educational metrics associated with career regret, exhaustion, empathy, tolerance, and educational debt.
• Demonstrated ability to develop the best predictors of educational success using mathematical models including high end classification and regression tree analysis and artificial intelligence algorithms.
• Demonstrated ability to embed educational lecture videos, and scientific publications into an AI based course to provide learner support for the academic mission.
• Demonstrated familiarity with tools like the Clinical Performance Instrument (CPI) used to assess competency for clinical practice.

Desired Qualifications

• At least one year of experience specifically working with a multi-centered academic educational database from the AAMC or like organization.
• Experience with the PT-GQ benchmarking system.
• PhD in Data Science with evidence of publications/presentations of educational research.
• Experience in trademarking new developments in educational research.
• Capacity to automate big data in genomic applications

Application Process: In order to be considered, applicants must upload a cover letter and resume (under submission relevant materials) that clearly address how they meet the listed required and desired qualifications of this position. 

Job openings are posted for a minimum of 14 calendar days. This job may be removed from posting and filled any time after the minimum posting period has ended. Successful candidates will be required to self-disclose any conviction history and will be subject to a criminal background check and credential/education verification. For questions, contact Anne Beyerink at anne-phillips@uiowa.edu.

This position is not eligible for University sponsorship for employment authorization.