Pros
- Performed healthcare analytics, statistical modeling, Python, R, and SQL analysis on 30,000+ NHANES, FNDDS, and clinical records to identify chronic disease and population health risk factors.
- Conducted trend analysis, cohort analysis, risk stratification, and public health research that identified 15–20% higher chronic disease prevalence in high-risk populations and supported evidence-based healthcare initiatives.
- Designed machine learning, predictive modeling, logistic regression, clustering, and data mining workflows on 1,500+ healthcare records to evaluate patient outcomes and healthcare utilization patterns.
- Developed automated data validation, ETL, data cleansing, HIPAA-compliant reporting, and quality assurance processes using Python, SQL, and Excel to improve reporting accuracy and efficiency.
- Built interactive Tableau dashboards, Power BI reports, data visualization, and executive presentations to communicate healthcare trends and deliver actionable insights to clinical and business stakeholders.
Cons
- Evaluated and implemented KPI/OKR frameworks, epidemiological analysis, and public health analytics for the City of St. Louis Department of Health across Behavioral Health and Environmental Health programs.
- Developed a centralized Smartsheet performance management system, automated reporting workflows, and dashboard reporting to improve data-driven decision-making and operational tracking.
- Analyzed healthcare and public health data using SQL, Excel, Tableau, statistical analysis, and data visualization to identify trends and support evidence-based initiatives.
- Optimized Performance Management (PM), Quality Improvement (QI), data governance, and reporting workflows to improve efficiency and standardize performance monitoring.
- Created executive reports and presentations using Tableau, Power BI, stakeholder reporting, and business intelligence tools to communicate KPI performance and strategic recommendations.