Healthcare Data Analytics & BI Analyst

University of Missouri Health Care; Columbia Township (MO); On-site; USD 68,000 - 111,000; Full time

Benefits offered by this job

Health insurance

Paid time off

Tuition assistance

Retirement options

Job summary

MU Health Care is seeking an analytics professional to develop, prepare, interpret, and monitor moderate-to-complex health care analyses, data projections, data models, and reports used by system management to guide decision-making.

Responsibilities include standardizing and cleaning data, building prediction models, developing metrics, and deploying actionable insights that improve patient outcomes using SQL, Excel, Power BI, Python, and other tools.

Qualifications

• Bachelor's degree required or equivalent experience.
• Two years of experience in decision support, analysis, or quality reporting.
• Preferred: Master's degree in related field.
• Experience with SQL, data visualization, and BI tools is strongly preferred.

Responsibilities

• Develops, interprets, and monitors health care analyses and reports for management decisions.
• Collaborates with stakeholders to deploy actionable insights that improve patient outcomes.
• Cleans and standardizes data, builds models, and develops metrics for complex projects.
• Performs ETL tasks and uses SQL, Python, R, and BI tools to produce meaningful analyses.
• Provides analytic guidance and technical support to analysts, managers, clinicians, and executives.

Skills

Analytical thinking

Communication

Collaboration

Problem solving

Data storytelling

Education

Bachelor's degree in health administration, public health, computer science, statistics, business administration, data science, or related field

Tools

SQL

Power BI

Python

Excel

Tableau

Job description

MU Health Care is seeking an analytics professional to develop, prepare, interpret, and monitor moderate-to-complex health care analyses, data projections, data models, and reports used by system management to guide decision-making.

Responsibilities include standardizing and cleaning data, building prediction models, developing metrics, and deploying actionable insights that improve patient outcomes using SQL, Excel, Power BI, Python, and other tools.