💥 Built Python/R data pipelines to preprocess and transform raw ICESat-2 and airborne LiDAR into model-ready datasets and canopy/structure metrics (5 m–100 m), with automated validation for schema, units, and completeness.

💥 Implemented reproducible QC workflows (logging, repeatable runs, cross-source reconciliation and drift/outlier checks), reducing manual review time by 30% and improving trust in downstream analysis.

💥 Cleaned and standardized service and survey data using Python and SQL. Defined KPIs and built repeatable reporting logic to support metrics-driven decision making.

💥 Developed Tableau/Power BI dashboards with consistent metric definitions and data-quality checks, reducing manual reporting effort by 40% and improving stakeholder visibility into trends.

💥 My coursework involves Mathematical Foundations for Data Science II, Applied Machine Learning, Data Science, Statistics, Integrated Product and Process Design

💥 CGPA: 4.00

💥 My coursework includes Machine Learning, Artificial Intelligence, Data Structures, Deep Learning, NLP, Computer Vision

💥 CGPA: 3.67