Engineered a robust clustering model to identify health systems from disparate locations, empowering health insurance clients to diminish coverage gaps through group contracting. This innovation streamlined network building and facilitated provider outreach needs. Devised trust-score metrics using string similarity algorithms to evaluate client data sources with Quest's verified data, enhancing data accuracy and reliability. Conducted root-cause analysis for product issues with development teams to implement updates and resolve identified issues promptly. Engineered a data-investigation tool that streamlined client contact method analysis, reducing report turnaround time by 80%. Leveraged Azure Data Factory and Azure Machine Learning Studio to create an end-to-end model calculating the time required to fill a client adequacy gap. Successfully deployed an OpenAI-based chatbot, reducing support calls and emails by 30%, and enhancing customer engagement. Created an Appointment Wait Time m

Data Scientist at Inspire 11

January 1, 2022 - April 1, 2022

Developed a semi-supervised clustering model analyzing 50,000+ SQL files to identify report similarities, resulting in a significant reduction in reports migrated to Azure-based Enterprise Data Warehouse. Engineered clustering using customer identification data and location, consolidating customers from multiple ERP systems into a unified global customer ID.

Senior Data Analyst at State Farm

December 1, 2018 - December 1, 2021

Developed various reports and metrics aimed at optimizing claims-handling operations, leading to substantial cost savings and process improvements. Implemented a logistic regression model to determine subrogation potential early in the claim-handling process, reducing claim iterations and improving efficiency. Automated data model changes from on-premises operational databases to AWS DynamoDB using AWS Lambda. Automated extraction of key metrics from claims analytics data to generate dashboards for stakeholder updates.