Healthcare Data Scientist II — Predictive Analytics

The University of Texas at Austin; Austin (TX); On-site; USD 48,000 - 80,000; Full time

Job summary

The University of Texas at Austin, Dell Medical School, seeks a Data Science Analyst II to partner with clinical, operational, and administrative leaders to develop advanced analytics, predictive models, and decision-support solutions that improve patient care and organizational performance.

You will design and deploy models, build ETL pipelines, create dashboards, and mentor junior analysts, collaborating with data architects, engineers, informaticists, and clinicians to ensure successful

Qualifications

• Master's degree with at least three years of professional experience in data science, predictive analytics, ML, or healthcare analytics.
• Strong SQL, data modeling, and Python programming skills.
• Experience developing ETL pipelines and working with cloud platforms (Azure, AWS, or Google Cloud).

Responsibilities

• Partner with clinicians, operational leaders, researchers, and administrators to identify analytical opportunities that improve patient care and operational performance.
• Design, develop, validate, and deploy predictive and machine learning models for clinical and operational initiatives.
• Build and maintain automated ETL pipelines and reproducible analytical workflows.
• Develop dashboards and reporting tools to support decision-making and KPI consistency.

Skills

SQL

Python

Data modeling

Machine learning

Analytics communication

Education

Master's degree in Data Science or related field

Tools

Cloud platforms (Azure/AWS/Google Cloud)

Job description

The University of Texas at Austin, Dell Medical School, seeks a Data Science Analyst II to partner with clinical, operational, and administrative leaders to develop advanced analytics, predictive models, and decision-support solutions that improve patient care and organizational performance.

You will design and deploy models, build ETL pipelines, create dashboards, and mentor junior analysts, collaborating with data architects, engineers, informaticists, and clinicians to ensure successful