Summary

The research data scientist will work with multidisciplinary PIs and the Department of Emergency Medicine faculty for the application of machine learning on bioinformatics data, related to use of Big Data and novel analytical tools to guide precision health and building machine learning/predictive models on health outcomes for a variety of illnesses in both pediatric and adult emergency medicine patients. The goals of our work will be to improve quality of care, value, and efficiency of patient care, leading to enhanced patient outcomes and population health. Deliverables will be focused in 3 primary areas: epidemiology and data acquisition, prediction models and risk stratification, and treatment optimization.

This is a part time position and will report to Vice Chair of Emergency Medicine.

Mission Statement

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.  Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.

Responsibilities*

To work with multidisciplinary PIs to develop machine learning and natural language processing applications with biostatistics and epidemiology to develop prediction tools for emergency department (ED) health outcomes and in resources utilization and aid the clinical decision making process.  The role and responsibilities will have an emphasis on study design and statistical methods including, but not limited to, include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, and meta-analysis.

Required Qualifications*

• Master’s related to information science, health informatics, applied data science, or machine learning; post-doc preferred.
• Has demonstrated experience and understanding of machine leaning services in AWS and/or Azure
• Has demonstrated experience and understanding of Python and R
• Personal commitment to high quality work and exceptional attention to detail
• Team player attitude with excellent organizational, multi-tasking skills and ability to work independently with minimal supervision and regularly exercise judgment in setting priorities.
• Excellent interpersonal and communication skills, both oral and written.

Work Schedule

The position is part time.  The work schedule is typically Monday-Friday, 4 hours daily. 

Work Locations

Domino Farms - REMOTE

Underfill Statement

This position may be underfilled at a lower classification depending on the qualifications of the selected candidate.

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Application Deadline

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.

U-M COVID-19 Vaccination Policy

COVID-19 vaccinations, including one booster when eligible, are required for all University of Michigan students, faculty and staff across all campuses, including Michigan Medicine.  This includes those working remotely and temporary workers.   More information on this new policy is available on the U-M Health Response website or the UM-Dearborn and UM-Flint websites.