Program Manager, Pharmacy Analytics and Billing Job Listing at MedStar Medical Group in Washington, DC (Job ID req35394)

Description

We are actively recruiting for an experienced pharmacy leader to join our team at MedStar Georgetown University Hospital in Washington, DC in the role of Program Manager, Pharmacy Analytics and Billing. The position will primarily be responsible for pharmacy analytics and billing, including but not limited to detailed review of medication use cycle to provide technical support in the design, implementation and ongoing monitoring of pharmaceutical charge and spend as well as workload and quality metrics for performance improvement. These functions are performed in accordance with all applicable laws and regulations and Georgetown University Hospital's philosophy, policies, procedures and standards.

• Bachelor's degree in Business, Informatics, or Engineering required
• Master's degree MBA preferred

• 5-7 years Experience in finance, performance management and metrics implementation, statistical analysis, visualization, and/or data analysis, preferably in a leadership role. Experience with mathematical process modeling, including discrete event simulation, linear programming, system dynamics, queuing theory and monte carlo simulation required
• 3-4 years Experience in a healthcare environment. 3 years' experience with operational and/or clinical data. experience using statistical computer languages and software (r, python, sql, minitab, etc.) to manipulate data and draw insights from large data sets preferred

• Lean Six Sigma Black Belt (SIXSIGBB) Upon Hire preferred

• Critical thinking, reasoning and problem solving.
• Strong interpersonal skills, organizational awareness and communication skills.
• Strong facilitation and presentation skills. Ability to understand business need(s) and conduct appropriate analysis to translate data into actionable insights.
• Advanced skill in MS Excel, MS Access, SQL and Tableau.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical hypothesis tests and proper usage, etc.) and experience with applications.