Revant Reddy

Designed scalable ETL pipelines with PySpark, Apache Airflow, and SQL Server to process millions of loan applications efficiently.

Built and validated machine learning models using Scikit-learn and Python, achieving an 11% reduction in loan delinquency rates.

Integrated Teradata, Snowflake, and AWS Redshift into Databricks for unified data analysis and comprehensive risk feature engineering.

Automated model deployment and monitoring through Jenkins and Git, ensuring compliance with regulatory audit requirements.

Developed Power BI dashboards to visualize credit risk metrics, aiding stakeholders in monitoring model performance and refining policies.

• Analyzed millions of healthcare claims with SQL, Python, and Excel, detecting duplicates and overpayments.
• Built interactive Power BI dashboards to track budgeting variances and repayment trends, providing weekly insights for $30M+ in monthly claims.
• Automated reconciliation workflows using Alteryx to integrate data from Azure Data Studio, increasing processing speed by 35%.
• Identified repayment delay patterns through time-series analysis in R and SAS, shortening average repayment cycles by 22%.
• Collaborated cross-functionally via Jira and Confluence to manage analytics sprints and maintain version-controlled documentation.
• Enhanced audit trail visibility for regulatory reporting compliance through improved data integration strategies.

Automated claims data validation processes using SQL, Python, and Snowflake, reducing claim denial rates by 18%.

Streamlined CPT and ICD code mapping to enhance clinical documentation accuracy.

Designed Power BI and Tableau dashboards to visualize claims trends, enabling identification of compliance gaps.

Implemented ETL pipelines with Talend and Informatica for EHR data extraction from Epic and Cerner systems.

• Generated Jupyter-based analyses and Excel reports for KPI monitoring in collaboration with finance and clinical teams.
• Participated in Agile sprint planning with Jira and Confluence to document analytics use cases effectively.