Medical Economics Data Analyst (Mid & Senior)

Key Responsibilities

• Manage analytic data needs for assigned business units and projects.
• Lead complex data analysis efforts and mentor other Data Analysts.
• Extract, load, model, and reconcile large datasets from multiple systems and sources.
• Analyze claims, provider, member, clinical, HEDIS, pharmacy, and other healthcare data.
• Review data to identify operational issues, trends, improvement opportunities, and recommend corrective actions.
• Develop and present analytical reports, dashboards, and insights for management decision-making.
• Perform data modeling using MS Excel, SQL, Access, and data warehouse tools.
• Conduct advanced data quality audits and analyses to ensure regulatory compliance.
• Support performance reporting and deliverables required by federal and state requirements.
• Design efficient SQL queries, create tables and indexes, and optimize stored procedures.
• Utilize programming languages (Python or R) for statistical and analytical modeling.
• Perform financial modeling, ROI analysis, claims pricing, and contract/network analysis.
• Support emerging trend analysis and predictive analytics for strategic planning.
• Collaborate with cross-functional teams to ensure consistent, accurate data usage.
• Train and guide junior analysts and assist in workload planning.

Required Qualifications

• Bachelor's degree in business, economics, statistics, mathematics, actuarial science, public health, health informatics, healthcare administration, finance, or a related field.
• Minimum of 5 years of experience with large databases, data verification, and data management, or at least 3 years of IT experience with a data focus.
• Strong SQL and query optimization skills (stored procedures, table creation, indexing).
• Proficiency with analytical tools such as MS Excel, Access, and data warehousing systems.
• Experience in healthcare analytics or related healthcare data domains (claims, clinical, provider, or pharmacy).

Preferred Qualifications

• Master's degree in a relevant field.
• Experience using Python or R for data analysis.
• Knowledge of data mining, trend analysis, and root-cause analysis techniques.
• Familiarity with business intelligence and visualization tools (e.g., Tableau, Power BI).
• Experience in financial modeling, contract/network analysis, and healthcare risk adjustment.
• Understanding of claims payment processes and healthcare compliance standards.