Sr. Analyst - Financial Operations
Listed on 2026-06-26
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IT/Tech
Data Analyst, Data Engineering, Data Scientist, Data Science Manager
We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time.
PositionSummary
Caremark LLC, a CVS Health company, is hiring for the following role in Woonsocket, RI:
Sr. Analyst - Financial Operations to develop and implement analytics and business intelligence reporting to measure and improve the effectiveness of various CVS Health programs and initiatives.
Duties include:
Leverage various analytical tools and skills, routine extraction, consolidation, summarization, visualization, quality review, and delivery of data from various and potentially disparate source systems. Perform ongoing management of intake requests, task/project assignment to staff, quality review, periodic status updates, and deliverable timeline management. Participate in the design, development, implementation, and ongoing maintenance of operations leadership facing dashboards that summarize and benchmark various revenue cycle performance benchmarks and deviations from expected/targeted trend.
Synthesize current business intelligence or trend data to support recommendations for action to create business intelligence tools or systems, including design of related databases, spreadsheets, or outputs. Develop large scale data structures and pipelines to organize, collect and standardize data that helps generate insights and addresses reporting needs. Write ETL (Extract / Transform / Load) processes, design database systems, and develop tools for real-time and offline analytic processing.
Transform data and integrate algorithms and models into automated processes. Analyze and synthesize data to meet the insights, reporting dashboard, and descriptive/predictive/prescriptive analytic requirements. Experiment with available software tools and advise on new tools to determine optimal solution given the requirements dictated by the model/use case. Support modeling/diagramming and build design specifications for data objects and surrounding data processing logic.
Collaborate with business solution strategists and support new data source onboarding process through data discovery, profiling, and mapping. Participate in the development of comprehensive workflow solutions that support the expansion our analytics strategy, including the development of process improvement and self-service data solutions, report automation, and exception monitoring innovations. Execute components of special projects as assigned. Build high-performance data processing frameworks leveraging cloud and/or on-premise data platform.
Participate in proof of concepts to build the data layers and concepts to derive analytical insights. Leverage multiple tools and programming languages to analyze and manipulate data sets from disparate data sources. Telecommuting available. Multiple positions.
- Master’s degree (or foreign equivalent) in Computer Science, Data Science, Statistics, Mathematics, Business Analytics, or a related field.
- Requires completion of a graduate-level course, research project, internship, or thesis in each of the following:
Data analytics on large data sets in healthcare, business, or retail sector; - Marketing analytics, clinical trials, or health research;
- Machine learning, statistical analysis, and predictive modeling;
- Programming in R, Hadoop, or Python;
- SAS or SQL programming languages;
- Visualization tools, including PowerBI or Tableau;
- Tools to automate CI/CD pipelines:
Jenkins, GIT, or Control-M; - Machine Learning or NLP (Scikit-Learn, Spa City, Pytorch, or Spark NLP);
- Designing data models and solutions for analytical and reporting use cases;
- Quantitative analysis techniques, including clustering, regression, and pattern recognition;
- Cloud components including cluster management, Kubernetes, or containerized services, storage, and workspace management;
- Relational…
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