Senior Data Analyst - Certification 3.0

Healthcare Analytics & Measure Development

• Analyze large healthcare datasets, including claims, administrative, clinical, and other real-world data sources.
• Support development, testing, validation, and implementation of quality measures, certification criteria, and performance indicators.
• Evaluatemeasurereliability, validity, risk adjustment approaches, benchmarking methods, and performance variation.
• Conductexploratoryanalyses toidentifyopportunities for new healthcare quality, safety, and certification products.
• Support environmental scans and market assessments of healthcare measures, datasets, and benchmarking programs.

Data Engineering & Platform Enablement

• Design, develop, andmaintainscalable analytical datasets and data pipelines.
• Work with cloud-based data platforms, including Databricks and related modern data architecture tools.
• Perform data ingestion, transformation, validation, and quality assurance across multiple healthcare data sources.
• Contribute to reusable data assets, documented data models, and reproducible analytical workflows.
• Support implementation of healthcare interoperability standards and emerging data exchange frameworks.

Insights, Communication & Product Support

• Translate complex analytical findings into clear business and product recommendations.
• Develop executive-ready presentations, dashboards, visualizations, and written summaries.
• Partner with product, clinical, business, and technology teams to define analytical requirements and support strategic decision-making.
• Communicate statistical and technical concepts to both technical and non-technical audiences.
• Support advisory councils, leadership discussions, and product development initiatives with data-driven insights.

Innovation & Future-Focused Analytics

• Evaluate new healthcare datasets, vendors, and data acquisition opportunities.
• Support predictive analytics, risk stratification, advanced modeling, and signal detection initiatives.
• Contribute to Cert 3.0 evolution through continuous learning and experimentation with emerging analytical methods and technologies.
• Stay informed about healthcare quality measurement, interoperability standards, AI applications, and industry best practices.

Analytically rigorous:Approaches problems with curiosity, structure, attention to detail, and a commitment toaccurate, reproducible work.

Technically adaptable:Comfortable learningnew technologies, data sources, and analytical methods; willing to develop deeper skills in data engineering, cloud technologies, and healthcare data platforms.

Healthcare-oriented:Understands the connection between data, quality improvement, patient outcomes, healthcare operations, and certification strategy.

Effective communicator:Creates clear data stories and translates technical findings into actionable recommendations for executives, clinicians, product leaders, and technical teams.

Collaborative team member:Works effectively across functions, gives and receives feedback constructively, and contributes to a positive, inclusive, mission-driven team culture., * Bachelor’s degree in Data Science, Statistics, Computer Science, Health Informatics, Epidemiology, Public Health, Mathematics, Healthcare Analytics,Economicsora related field.

• 3-7+ years of experience in healthcare analytics, healthcare data management, healthcare data science, or a related analytical role.
• Experience working with healthcareclaimsdata, including Medicare, Medicaid, commercial, administrative, or value-based care datasets.
• Strong SQL skills and experienceoptimizingqueries for large datasets.
• Experience in performing analytics in a cloud-based computing environment, such as Databricks.
• ProficiencyinR (preferred) or Pythonfor data management, analysis, and reproducible workflows.
• Experience building analytical datasets, performing data quality validation, and documenting assumptions and limitations.
• Strong analytical, problem-solving, andcritical thinkingskills.
• Demonstrated ability to communicate complex findings effectively to diverse audiences.
• Experience working collaboratively within cross-functional teams.

Preferred

• Experience with CMS’s Virtual Data Research Center (VRDC)environment.
• Experience specific to Databricks analytic platform.
• Experience with healthcare data standards and interoperability frameworks such as FHIR, HL7, or CQL.
• Familiarity with EHR data sources and clinical data models, including Epic, Oracle Health/Cerner, Cosmos, or comparable environments.
• Experience developing, testing, or evaluating healthcare quality measures.
• Knowledge of risk adjustment, benchmarking, population health analytics, outcomes measurement, or value-based care programs.
• Familiarity with healthcare terminology and coding systems, including ICD-10, CPT, HCPCS, DRG, SNOMED, LOINC, orRxNorm.
• Experience using Power BI, Tableau, Shiny, or similar visualization tools.
• Experience with Git, CI/CD, code review, and modern software development practices.
• SAS knowledge is a plus

This job description is intended to describe the general nature and level of work performed by an employee assigned to this position. The description is not an exhaustive list of all duties, responsibilities, knowledge, skills, and abilities, and working conditions associated with this position. All requirements are subject to possible modification and reasonably accommodate individuals with disabilities.