I am a data scientist with a PhD in Astronomy and experience building time series forecasting, anomaly detection, and NLP solutions across multiple industries. My work spans developing end-to-end, large-scale time series forecasting pipelines, designing anomaly detection models for time series data, and building text analytics solutions. Across these projects, I work with a broad set of tools, including Python, SQL, Airflow, XGBoost, scikit-learn, and TensorFlow.
I am currently expanding my focus into GIS and remote sensing, with an interest in combining machine learning and geospatial data. I am adding more tools to my workflow, including GeoPandas, Shapely, Rasterio, Leafmap, QGIS, PostgreSQL (including PostGIS), DuckDB, and Google Earth Engine.
This portfolio is still a work in progress as I continue polishing and adding more projects.