Dileep Reddy Email and Phone Number

• Processed and analyzed 10+ terabytes of structured and unstructured healthcare data, including claims data, EHR, and prescription metrics, ensuring seamless data integrity across multiple sources.• Integrated 1M+ daily records from SQL databases, APIs, and cloud platforms using ETL pipelines built with Python and Airflow, automating data workflows and reducing manual errors.• Developed predictive models for high-risk patients, improving early intervention rates by 20%, and achieved 92% accuracy in predicting medication non-adherence using machine learning tools like scikit- learn.• Conducted advanced statistical analysis (ANOVA, regression modeling) to measure the effectiveness of health interventions, enabling data-driven clinical decision-making.• Created interactive dashboards in Tableau and Power BI, reducing hospital readmissions by 25% and improving inventory management for 3,000+ pharmacy locations by 15% using time-series forecasting.• Spearheaded a $5M operational cost reduction by optimizing resource allocation and improving efficiency in hospital partnered programs through actionable data insights.• Enhanced patient satisfaction metrics by analyzing 500K+ survey responses, identifying key improvement areas, and driving measurable service enhancements.• Collaborated with clinical teams to define impactful variables like comorbidity indices and social determinants of health, ensuring 99.9% data accuracy through robust validation scripts.

• Developed predictive models using Python and R, improving forecasting accuracy by 15% for product lifecycle metrics, driving data-driven decision-making.• Conducted statistical analysis and hypothesis testing using Python and SAS, reducing product defect rates by 20% through the identification of key factors influencing failures.• Optimized 50+ complex DAX measures and calculated columns weekly to perform in-depth statistical analysis.• Leveraged Power BI's DAX functions to create calculated columns, measures, and complex metrics for deeper insights into key KPIs such as revenue per customer, profit margins, customer lifetime value (CLTV), and employee performance metrics.• Optimized SQL queries and data models, improving query performance and enabling quicker insights and data retrieval for the analytics team.• Applied machine learning techniques (regression, clustering, classification) to analyze customer behavior, achieving an 18% increase in customer retention and improved targeting strategies.