GIS Data Scientist

AgreeYa Solutions•Temple Terrace, FL

1d•Hybrid

About The Position

AgreeYa is seeking a highly skilled GIS Data Scientist / Analytics Engineer to join the Data Science Development team within Network Planning. The ideal candidate should possess strong hands-on expertise in geospatial data science, Python, large-scale spatial datasets, network modeling, and graph/routing algorithms. The role will focus on developing and optimizing spatial network models, implementing algorithms such as Dijkstra, Kruskal, and Minimum Spanning Tree (MST), and working with large customer, inventory, and network datasets to support network planning and optimization. The candidate will leverage QGIS, ArcGIS Pro, ArcGIS/Esri frameworks, AWS/GCP, and spatial data technologies to analyze and visualize complex geospatial information and deliver scalable modeling solutions. Prior telecom or FTTH experience is a plus but not mandatory; strong hands-on experience with spatial analytics, graph algorithms, Python, and large datasets is the primary focus. We are seeking a highly skilled and results-driven Data Scientist / Analytics Engineer to join our Data Science Development team within Network Planning. In this role, you will be responsible for the core modernization, evolution, and scaling of our geospatial and statistical fiber modeling platforms—specifically our proprietary automated routing, network optimization, and Feeder Distribution Hub (FDH) modeling engines. You will be responsible for architecting and unifying complex network routing engines, scaling high-throughput geospatial datasets, optimizing cloud database performance, and delivering high-impact spatial analytics that directly guide our national broadband infrastructure strategy.

Requirements

Bachelor's or master's degree in computer science, Data Science, Geographic Information Systems (GIS), Operations Research, Software Engineering, or a related quantitative field.
5+ years of experience in data engineering, geospatial analytics, or data science roles, with demonstrated leadership in technical delivery.
Proficiency in Python (3.x), SQL, and microservices/web execution frameworks (Flask, FastAPI, or Django) a must.
Deep expertise with spatial analytics tools, spatial SQL, PostGIS, ESRI/ArcGIS frameworks, and open-source mapping platforms (e.g., Overture Maps, QGIS, PG Tile Server).
Demonstrated experience with transactional and analytics databases (PostgreSQL, Oracle, Redshift, BigQuery).
Hands-on experience with Apache Airflow, Docker, AWS (EC2/RDS), and GCP (BigQuery, Cloud Storage).

Nice To Haves

Prior telecom or FTTH experience
Direct experience in telecommunications network planning, fiber infrastructure deployment (FTTH, FWA), or complex graph/network routing algorithms.
Proven track record of system performance tuning, query optimization, and cost-reduction initiatives across enterprise cloud environments.

Responsibilities

Design, build, and optimize spatial routing and network planning models across our core automated planning platforms and fiber mapping engines.
Drive the strategic software convergence of legacy spatial planning scripts into a unified, high-performance network modeling stack.
Execute large-scale Feeder Distribution Hub (FDH) and Fiber-to-the-Home (FTTH) remodeling using Djikstra, Kruskal or other minium tree spanning algorithms utilizing network topology, and spatial datasets.
Transition and standardize spatial graph processing and routing engines
Ability to handle large datasets in spatial data formats (e.g., shapefiles, GeoJSON).
Integrate advanced spatial datasets (open street maps, enterprise address databases, and master location repositories) into automated ETL pipelines for high-accuracy routing and spatial analysis.
Oversee production relational and columnar databases across cloud environments.
Lead continuous database optimization initiatives—automating table maintenance, rebuilding high-traffic spatial reference cross-reference tables, and tuning long-running queries to maintain low latency.
Build and manage automated workflow pipelines (e.g., Airflow/Python) to streamline real-time data sharing across cross-functional engineering, analytics, and AI teams.
Implement self-healing scripts, system telemetry monitoring, and strict role-based access security across the modeling stack.
Architect secure, scalable, and cost-effective data infrastructure environments across multi-cloud environments (AWS and GCP).