Nilesh Katoch

• Predictive ROI & Patient Engagement: Led end-to-end analytics for the Patient Engagement Platform, architecting Predictive
ROI models to forecast patient return from multi-channel campaigns.
• Predictive Health Analytics: Architected a Chronic Disease Prediction engine (Diabetes/Hypertension) using Python and Scikit
learn to forecast patient risk profiles and enable proactive clinical intervention.
• Revenue Leakage Prevention: Led an end-to-end initiative using PySpark and Azure Databricks to analyze patient journeys and
identify billing gaps, recovering $500k+ in leaked revenue.
• Segmentation & Forecasting: Built a Patient 360 ecosystem using RFM clustering to predict high-value segments and forecast
preferred healthcare brands, increasing multi-channel engagement ROI.
• Technical Leadership: Mentored junior analysts in advanced SQL and ML workflows, improving overall team productivity and
delivery speed by 20%.

• Developed and deployed a Predictive Risk Model utilizing Python and Scikit-Learn, achieving 85% accuracy in identifying high-risk insurance claims and significantly reducing false positives in fraud detection.
• Engineered an automated ETL pipeline with Python (Pandas) and SQL, optimizing data extraction and transformation from Legacy Mainframe systems and reducing data retrieval time by 70%.
• Designed and implemented interactive Power BI dashboards that integrated statistical forecasts to visualize claim trends, empowering leadership with actionable insights for strategic decision-making.
• Leveraged data analysis techniques to drive data-driven solutions, enhancing operational efficiency and supporting business objectives in the insurance sector.
• Collaborated with cross-functional teams to translate complex data into meaningful insights, fostering a data-centric culture within the organization.

• Predictive Risk Modeling: Modernized Fraud, Waste, and Abuse (FWA) detection by building predictive models (Logistic
Regression/Decision Trees) that flagged high-risk insurance claims with 85% accuracy.
• ETL &Automation: Engineered custom ETL pipelines to migrate transactional data from legacy mainframes to real-time BI
environments, eliminating 70% of manual reporting overhead.
• Diagnostic Analytics: Developed automated Power BI dashboards with advanced DAX to provide real-time visibility into claim
risk trends and outlier patterns.
• Led the technical upskilling of the team, transitioning reporting workflows from legacy green-screen systems to a modern, self
service Power BI analytics stack.

• Engineering Foundation: Managed end-to-end SDLC for insurance backend systems, maintaining 99.9% uptime and optimizing
database reliability for high-volume transactions.
• Data Modernization: Drove the transition to modern analytics by developing advanced SQL reporting and automating initial
data pipelines for stakeholder dashboards.