- Led project life cycle improvements on in-house reserve model, resulting in the successful planning and deployment of a flexible, modular application used currently for month-end valuations- Orchestrated monthly reserve valuation workflow including source data updates, data quality assessments, gathering operational factors and considerations, reserve setting and cross-functional communication of results- Assessed and communicated key data and functionality requirements for new data warehousing environment to external consultants over project life cycle- Led user acceptance testing of new data warehousing environment for the entire finance vertical, ensuring accuracy and trust in data sources- Migrated existing critical data pipelines to new environment, leveraging environment features to improve efficiency and accuracy- Planned, built, and implemented data pipeline to allow for granular slicing and reporting of cost and revenue financials on incurred and GAAP basis- Provided guidance to team members on model building best practices, applicable programming skills and relevant domain expertise- Utilized subject matter expertise of claims data system to provide critical reconciliation of financial data sources in support of year-end audit

- Built and utilized proprietary IBNR reserving model for month-end/quarter close reserve estimates- Built and utilized back-testing framework to assess quality of IBNR estimation methods and inform reserve setting decisions- Built and utilized MCR (Medical Cost Ratio) reporting for month-end/quarter close and communicated results, restatements and drivers- Created curated data sources and tooling to provide stakeholders easy access bi-temporal reporting on CMS Part C/D revenue components and enrollment records- Engineered data pipelines in support of financial reporting, IBNR modeling and enrollment reporting- Supported and ran enrollment projection models in support of budget/forecasting- Developed medical cost benchmarking study to compare and contrast medical expense against industry benchmarks- Built flexible tools and reporting for budget vs actuals analysis- Generated pricing assumptions, participated in benefit design, and supplied data feeds in support of the Medicare Bid

- Planned and developed interface and backend model components of proprietary pricing model using Python and JavaScript in support of future in-house Medicare bid pricing activity with modular expansion of capabilities- Led transition and development of budget expense forecasting model from Excel onto SQL Server/VBA based architecture, resulting in significant processing time reduction and improvements to interface/usability- Expanded automation of data processing and reconciliation against financials using SQL Server Tools/Python and cross functional partnering with IT resulting in significantly decreased overhead and improved support for audits by regulating entities- Conducted presentations and advocated for wider department adoption of Python as part data and analytics toolset- Participated in annual Medicare bid cycle data and assumption preparation, and worked on reconciliation of financials in support of bid data audit- Developed month and quarter-end financial forecasts and accruals- Ad hoc reporting in support of vendor RFPs and bid preparation- Gained exposure and domain knowledge in Medicare Advantage and Part D, as well as Medicare-Medicaid Plans

- Wrote microservices in Go to extract and transform data from relational databases and APIs and performed analysis to support operational reporting and ad hoc projects.- Arranged meetings and worked closely with managers to understand and identify operational bottlenecks; provided and analyzed the effectiveness of workflow changes through programming visual reporting solutions.- Spearheaded initiative to improve user interface of internal dashboards, leading to improved efficiency and half the load time.- Created and maintained dashboards for each department, giving team leads clear visibility on important team KPIs.- Developed application to schedule and automate extraction and transfer of data from SQL Server to Google BigQuery.