Geospatial Data Scientist working end to end — computer vision on drone imagery and LiDAR, AWS cloud pipelines and infrastructure-as-code, and the AI agents that tie it together — from field survey to production platforms protecting critical utility infrastructure.
I work at the intersection of geospatial engineering, applied AI, and cloud data platform engineering — currently as a Data Scientist at The Spear Group, technical lead across Florida Power & Light's AI-based infrastructure inspection program. I set technical requirements, build the data pipelines and cloud infrastructure behind the program, and serve as subject-matter expert through vendor model development. I also build the GIS applications used for infrastructure monitoring and disaster response — including a portal used during Hurricane Milton.
My background started in the field: GNSS and UAV survey, municipal geodatabase design, and control surveys as a Geomatics Engineer in Nepal — before graduate research at Florida Atlantic University on flood hazard modeling and environmental justice sharpened the AI/ML and geospatial data engineering side. That research led to a peer-reviewed publication on flood hazard exposure and social vulnerability in the U.S., and I'm a co-author on PyWMP, an open-source watershed modeling framework, with the accompanying manuscript currently under peer review.
Outside of work I contribute to open-source geospatial and AI tools — a published Python package for road-centerline extraction, agentic AI systems for querying spatial and hazard data, and a flood/storm-surge exposure pipeline for utility assets, among others below. I'm FAA Part 107 certified.
Technical lead across FPL's AI and geospatial infrastructure inspection program — from vendor selection through data engineering to production deployment.
Open-source contributions built outside of my employment, focused on hazard-resilient geospatial AI and critical infrastructure — a published Python package, agentic AI over spatial/hazard data, and full data-to-map pipelines. Most ship with a live demo; nothing here requires an API key to try.
A published Python package that extracts road centerlines from polygon geometries — worldwide, in any input CRS, in any format GeoPandas can read. CLI and Python API, computed via medial-axis skeletonization.
An MCP server unifying FEMA, USGS, NOAA, NWS, and NASA hazard data behind a single AI-agent interface — one-call location risk summaries and map rendering, compatible with Claude Desktop.
A cloud-native pipeline overlaying NOAA storm-surge and flood-inundation data against Overture Maps building footprints, scoring per-asset flood exposure across 8 coastal regions — with a map and API.
Generates DEM, DSM, hillshade, slope, aspect, and contour products from USGS 3DEP lidar. Draw an area of interest in the browser; it generates a configured local script — no data ever uploads.
Ask a plain-English question about San Francisco's utility infrastructure — a local LLM agent (LangGraph ReAct, Ollama) translates it to spatial SQL, runs it, and answers with a summary and interactive map. No API keys.
Converts a spreadsheet of US street addresses into geocoded points and building footprint polygons using free Nominatim and Overpass APIs — as a no-install web app or a scriptable Jupyter notebook.
Data-engineering and analytics platform for GTFS transit feeds — headways, stop coverage, and spacing anomalies, explored in an interactive map app.
3D-style land cover export from Sentinel-2 tiles, clipped to any country and automated via GitHub Actions — a modernized take on the rayshader R workflow.
Converts romanized Nepali to Devanagari live, client-side, plus a CLI that batch-translates SRT subtitles through pluggable backends.
LangChain RAG chatbot with a history-aware retriever for multi-turn, context-grounded conversation, citing sources. Runs locally via Ollama.
The Experience section above covers the program-level work — this is a few platform-layer components that don't fit neatly into a resume bullet.
AWS CDK component framework with enterprise naming, tagging, and deployment abstractions for ML and app workloads.
ONNX-based object detection package for utility equipment with COCO JSON output and optional visualization.
PostgreSQL schema migration CLI with source/target connection separation, dry runs, and preflight checks.
Reusable package for API extraction, taxonomy joins, metadata patching, and downstream publishing automation.
A small browser-side terrain model in the spirit of the FEMA-based hydrologic exposure work below — procedurally generated elevation, not the published dataset. Drag the slider to raise sea level and watch inundation spread.
Color ramp: low-lying green → upland tan/white. Blue = below current sea level.
Utility corridor: 0 of 7 poles below sea level
Most of my professional work is confidential to the organizations I've supported — publications below are what I can share in full.
MSc, Geosciences — GIS Concentration · Florida Atlantic University · 2023–2024 · GPA 4.0/4.0
BSc, Geomatics Engineering · Tribhuvan University, Nepal · 2016–2021 · GPA 3.64/4.0
Open to Data Science, GIS/Geospatial Engineering, and Data/Platform roles. Based in West Palm Beach, FL.