Analyzed 25M+ healthcare and operational records across claims, provider, and customer datasets to identify cost drivers and performance gaps, contributing to an estimated 18% reduction in processing delays.

Designed and delivered 10+ executive and operational dashboards in Tableau and Power BI, actively used by 50+ stakeholders for weekly performance reviews and decision-making.

Built and optimized complex SQL queries (100k+ rows per run) and Python data pipelines, improving data accuracy by ~30% and reducing manual reconciliation effort.

Conducted A/B testing, hypothesis testing, and regression analysis to evaluate process changes, supporting data-backed recommendations adopted by leadership.

Defined and standardized 20+ KPIs in collaboration with product, operations, and finance teams, ensuring consistent performance measurement across business units.

Automated recurring reports and validation checks, reducing manual reporting time by ~40% and improving turnaround for stakeholders.

Developed predictive forecasting models to support capacity planning and demand trends, improving short-term planning accuracy.

Established data quality checks and documentation standards, reducing downstream reporting issues and rework.

Delivered analytics and reporting solutions for 8+ enterprise clients across healthcare, retail, financial services, and technology sectors.

Translated ambiguous business problems into scalable data models and insights, directly supporting strategic recommendations presented to senior client leadership.

Developed Python automation scripts integrated with SQL databases, processing 5M+ records per engagement with ~98% extraction accuracy.

Built client-facing dashboards and reports tracking operational, financial, and growth KPIs, enabling executives to monitor performance in near real time.

Reduced reporting errors by ~25% by implementing data validation frameworks, monitoring checks, and Git-based version control.

Streamlined Excel- and VBA-based reporting workflows, cutting analysis turnaround time by 35–40% across multiple engagements.

Identified inefficiencies in client operations through data analysis, contributing to cost optimization and process improvement initiatives.