Clinical Data Scientist - ML & EHR Analytics (Hybrid/Remote)

University of Michigan; Ann Arbor (MI); Hybrid; USD 110,000 - 150,000; Full time

Benefits offered by this job

Excellent medical, dental, and vision

2:1 retirement match

Job summary

University of Michigan is seeking a full-time data scientist to support clinical research in acute respiratory disease and other critical illnesses. You will join a cross-disciplinary team of physicians, computer scientists, and trainees, reporting to the PI, and contribute to machine-learning projects in critical care.

The role emphasizes independent work, rigorous data management, and collaboration with researchers.

Qualifications

• Master's in CS/biomedical informatics or related field; bachelor's with relevant coursework considered.
• Fluency in SQL for database development and manipulation.
• Fluency in Python or R for data management and analysis.
• Excellent communication skills, written and verbal.
• Strong organizational and documentation skills with attention to detail.
• 5–7 years of systems analysis/programming activities in data management/analytics in a business environment.
• 3+ years in systems analysis/programming activities in data management/analytics in a business environment.

Responsibilities

• Perform advanced SQL queries of institutional data warehouses to create analytic datasets from EHR data.
• Develop statistical and machine-learning models for healthcare data to support clinical care.
• Create and maintain documentation of programs and models.
• Contribute to research reports for conferences, manuscripts, and proposals.
• Participate in regular multidisciplinary research team meetings.

Skills

SQL

Python

R

Education

Master's degree in CS/biomedical informatics or related fields

Tools

Linux/Unix

Job description

University of Michigan is seeking a full-time data scientist to support clinical research in acute respiratory disease and other critical illnesses. You will join a cross-disciplinary team of physicians, computer scientists, and trainees, reporting to the PI, and contribute to machine-learning projects in critical care.

The role emphasizes independent work, rigorous data management, and collaboration with researchers.