– Led data development for a client onboarding dashboard by assigning specialties to 2.1L+ Group TINs using multiple healthcare data sources, optimizing large-scale datasets through normalization techniques.– Optimized large-scale healthcare data processing workflows, reducing execution time from 3 hours to 20 minutes using Python, Pandas, and Polars on 40–45 GB vendor datasets.– Managed NPI scoring for 2.2M+ healthcare providers and developed Tableau dashboards for QC, validation, and executive-level reporting.– Built Python-based ETL pipelines to process and standardize 10+ GB of CMS (NPPES, PECOS, NDF, PUF) and Medicaid datasets into analytics-ready formats, enabling downstream provider scoring and healthcare analytics workflows. 

Automated web data extraction for 10+ Medicare Advantage payors using UiPath, enabling structured datasets for business insights and improving analysis efficiency by 40%. Delivered high-impact contributions during internship and earned early conversion to a full-time Associate Product Analyst role based on performance. 

Developed testing protocols and performance metrics for cold plasma-based equipment, leading to a ⚡ 30% improvement in safety compliance and enhanced overall efficacy. 🔬✅ 

📊 Analyzed a 50K-record retail dataset to identify profit gaps, revealing a potential 15% revenue boost by shifting focus to high-margin segments. 🚀 Used Python, Pandas & Seaborn to create visual profit maps, uncovering 204 deals per state and 19 per city. 🔍📈