Global Appropriateness Measures LLC Senior Data Analyst, Cost Remote · Full time Company website

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GAM is a consortium of physicians dedicated to advancing patient outcomes through data-driven insights and innovative metrics rooted in clinical expertise. We are the gold standard in healthcare appropriateness measurement, and our primary focus is on measuring clinical waste—a priority area for health systems, health plans, self-funded employers, and data analytics companies. We’re a fast-moving, high-growth company helping people navigate healthcare smarter - saving money, avoiding unnecessary care, and getting better outcomes. We partner with employers and other industry leaders to help their teams make better healthcare decisions.

KEY RESPONSIBILITIES

MRF & Claims Analysis

• Analyze payer and hospital machine-readable files and develop methods to assess the credibility of prices.
• Leverage claims data to validate the provision of services by providers in the machine-readable files.
• Work with price transparency data to support cost analysis for GAM’s provider directory and platform.
• Contribute to translating MRF analysis into data products that can be brought to market.

Data Quality & Validation

• Identify anomalies, gaps, and inconsistencies across MRF datasets.
• Support QA processes to ensure data integrity in downstream products.
• Document findings and methods for both technical and non-technical audiences.

Cross-Functional Collaboration

• Partner with data engineers and product teams to translate analytical findings into actionable outputs.
• Contribute to data definitions, methodology documentation, and MRF-based logic used across the platform.
• Participate in client or vendor conversations as needed as a subject matter expert.

EDUCATION 

• Bachelor's degree in Statistics, Biostatistics, Public Health, Health Informatics, Computer Science, Mathematics, or a closely related quantitative field. 

REQUIRED QUALIFICATIONS

• Direct experience with MRF and price transparency data, including a working understanding of payer and hospital file structures, limitations, and analytical use cases.
• Deep hands-on experience with claims analysis across commercial, Medicare, and Medicaid, including professional and institutional claim types, revenue codes, CPT/HCPCS codes, DRGs (MS-DRGs and APR-DRGs), and payer and population-specific nuances.
• Proficiency in SQL.
• Experience with Python for data analysis and manipulation.
• Exposure to Databricks or similar cloud data platforms.
• Strong critical thinking skills.
• Attention to detail and comfort working with large, complex, real-world datasets.
• Ability to communicate findings clearly to both technical and non-technical stakeholders.

PREFERRED QUALIFICATIONS

• Familiarity with CMS data.
• Experience in payer, provider, or health IT/vendor environments.