Monika Botta

Analytical Data Scientist with over 2 years of experience in insurance analytics, focusing on dynamic pricing, machine learning, and business intelligence. Developed end-to-end data solutions, including ETL pipelines, predictive models, and Power BI dashboards. Skilled in interpreting large, multi-source datasets to deliver actionable business insights.

• Advanced certification in Data Science and Artificial Intelligence, IIT-Madras and Intellipaat
• NSDC (National Skill Development Council) and Skill India, Internshala, 08/01/23 - 09/30/23

• Built dynamic pricing systems for 4+ insurance products, enabling automated premium calculations across multiple geographies.
• Reduced reporting turnaround time by ~40% through Power BI dashboards and automation.
• Designed data models (25+ tables), improving analytics efficiency and data accessibility.
• Developed ETL pipelines integrating multi-source datasets, enabling reliable and scalable analytics workflows.

• Insurance Pricing & Risk Engine (Flagship Project), Built a scalable actuarial pricing system integrating risk factors, underwriting rules, weather data, and real-time business inputs., Automated premium and sum insured calculations across multiple crops, policy structures, and geographies., Developed modular Python pricing algorithms incorporating indemnity logic, burning cost calculations, and weighted premium analytics., Improved pricing consistency and reduced manual dependency through automated workflows and validation pipelines.
• Weather Index Insurance Payout Analytics System, Built a large-scale analytics pipeline to compute weather-index insurance payouts using multi-year district and mandal-level weather datasets., Implemented advanced time-series analytics including rolling windows, spell detection, threshold deviations, and phase-wise aggregation., Designed regex-based rule parsing engines to translate policy terms and business rules into machine-readable payout logic., Optimized scalable payout computations using vectorized Pandas workflows and automated reporting pipelines.
• Shakambari Dynamic Pricing Engine, Designed and implemented an end-to-end actuarial risk pricing engine for crop insurance using historical weather and peril datasets., Developed stage-wise crop growth and peril exposure models incorporating cyclone, flood, hailstorm, fire, and inundation risks., Built configurable underwriting workflows including premium caps, indemnity severity curves, and policy validation logic., Delivered audit-ready pricing outputs and reconciliation workflows aligned with actuarial governance standards.
• Crop Monitoring & Climate Data Automation System, Developed Selenium-based automation tools for scraping agricultural and climate datasets from government portals., Built GUI-driven Python utilities for Excel consolidation, validation, transformation, and automated reporting workflows., Automated ingestion and preprocessing of NDVI, rainfall, soil moisture, and drought-monitoring datasets., Reduced manual data preparation effort through reusable ETL and automation pipelines.
• Insurance Claims Analytics Dashboard, Developed interactive Power BI dashboards tracking claims ratio, fraud indicators, segmentation insights, and financial KPIs., Reduced reporting turnaround time by ~30–40% and improved stakeholder decision-making efficiency.