Accomplished Senior Data Scientist at Tavant Technologies Inc, specializing in machine learning and geospatial analysis. Spearheaded innovative solutions that enhanced operational efficiency and yield forecasting, achieving significant cost reductions. Adept at translating complex data into actionable insights, demonstrating strong analytical skills and effective communication in cross-functional teams.
Led the development of an object detection solution using UAV imagery (RGB, NIR, Red band) to detect mature and immature pineapples across 26,000+ hectares.
• Implemented EfficientDet-based detection with Reinforcement Learning for bounding box refinement, improving localization accuracy in complex field environments.
• Translated detection results into actionable insights for labor allocation, yield forecasting, and supply chain optimization, reducing manual effort and improving operational efficiency..
Developed an intelligent agent enabling natural language interaction with multispectral UAV data for crop health, temporal analysis, and yield forecasting.
• Structured agricultural data into knowledge graphs using LangGraph to support explainable, context-aware insights.
• Extended the Pineapple Object Detection solution by providing an intuitive interface for agronomists and operations teams to query field analysis, enhancing decision-making and operational efficiency.
Developed advanced data-driven solutions to cluster warranty claims and identify suspect claims in manufacturing, improving quality control and reducing financial risk.
• Applied clustering and statistical feature engineering to group similar claims and identify anomalies.
• Built models to flag high-risk claims, improving investigation efficiency and reducing warranty costs.
• Enabled data-driven insights for quality control and operational decision-making.
Developed a precision harvesting system using reinforcement learning to optimize harvesting periods based on weather and other factors, increasing tea yield by 35% and quality by 20%.
• Achieved 30% reduction in operational costs through adaptive, cost-efficient harvesting strategies.
• Ensured robust performance under varying weather conditions, improving sustainability and consistency.
Implemented hyperspectral data analysis techniques to achieve granular and precise product traceability throughout tea manufacturing process.
• Developed and deployed algorithms for the accurate quantification of disease percentage, coarse percentage and moisture content enabling rapid intervention and reducing the risk of contaminated or compromised products.
• Algorithms use: PCA, SVM, XGBoost.
Demonstrated a reduction in disease management costs through targeted interventions. By focusing resources on high-risk sectors, the project led to a 18% decrease in overall disease management expenses.
• Provided tea estate managers with actionable insights to optimize resource allocation. By anticipating disease probabilities, resources such as pesticides and manpower are allocated more efficiently, contributing to cost savings.
• Algorithms used: Gradient Boosting