Risk Adjustment Informatics Manager: Data & Analytics

CVS Health Corporation; Olympia (WA); On-site; USD 66,000 - 146,000; Full time

Job summary

CVS Health is seeking an experienced analytics professional to join the Revenue Integrity Informatics Valuation Analytics team. You will quantify financial and risk score impacts of Medicare Advantage risk adjustment interventions and support enterprise decision-making with data-driven insights.

You will develop scalable analytical solutions, maintain CMS models in Python and cloud environments, and collaborate with cross-functional teams to enable informed program optimization.

Qualifications

• 5+ years of experience in healthcare analytics, data management, data engineering, reporting, or business analytics.
• 3+ years of experience writing complex SQL queries in cloud-based environments such as Big Query, Databricks, AWS, or similar platforms.
• Experience developing and supporting automated reporting, production processes, or data pipelines.
• Experienced using GitHub or other source control tools to manage code repositories and version control.
• Experience working with healthcare claims, enrollment, provider, diagnosis, finance, or risk adjustment data.
• Demonstrated ability to communicate technical concepts to both technical and non-technical stakeholders.

Responsibilities

• Develop, enhance, automate, and maintain standardized monthly program evaluation and KPI reporting using claims, enrollment, diagnosis, provider, and risk adjustment data from multiple enterprise and government data sources.
• Develop and maintain member-level risk adjustment datasets, risk score files, and support analytic outputs for Medicare Advantage and ACA programs.
• Maintain, validate, enhance, and document CMS risk adjustment models in Python and Databricks, including annual model updates, hierarchy logic, interaction logic, reconciliation activities, and support for downstream analytics users.
• Design, develop, and maintain cloud-based datasets, automated data pipelines, and analytical infrastructure within Google Cloud Platform (BigQuery), Databricks, AWS, and Python environments.
• Develop, automate, and streamline processes used to share risk adjustment valuation data, reporting outputs, and reference datasets with business and reporting partners.
• Lead and support migration of analytic processes and data assets from SAS to cloud-based solutions, including BigQuery, Databricks, AWS, and Python.
• Monitor data quality across source systems, reference datasets, and reporting outputs; identify root causes of issues and partner with stakeholders to implement sustainable solutions.
• Own and support recurring monthly and ad hoc production processes, including monitoring, quality control, troubleshooting, issue resolution, and process documentation.
• Maintain and improve reference datasets, business rules, and metadata supporting risk adjustment models, valuation processes, KPI reporting, and program analytics.
• Partner with Informatics, Revenue Integrity, Actuarial, PMO, Data Engineering, and other stakeholders to gather requirements and deliver scalable analytical solutions.
• Research and respond to complex ad hoc questions related to risk adjustment program performance, valuation methodologies, and healthcare financial outcomes.

Skills

Healthcare analytics

SQL queries

Communication

Data modeling

Data storytelling

Education

Bachelor's degree in Analytics, Informatics, Statistics, Mathematics, Computer Science, Healthcare Administration, Economics, Finance, or equivalent work experience

Tools

BigQuery

Databricks

AWS

SAS

Python

Job description

CVS Health is seeking an experienced analytics professional to join the Revenue Integrity Informatics Valuation Analytics team. You will quantify financial and risk score impacts of Medicare Advantage risk adjustment interventions and support enterprise decision-making with data-driven insights.

You will develop scalable analytical solutions, maintain CMS models in Python and cloud environments, and collaborate with cross-functional teams to enable informed program optimization.