Geospatial Data Scientist

We bring clarity to physical risk, enabling leaders to engage with confidence and enact resilient business critical decisions. Today's environmental, economic, and infrastructure challenges are deeply interconnected, yet the data required to understand these relationships is scattered across siloed and aging systems. Neural Earth enables operational execution; delivering a single decision intelligence platform that unifies planetary, governmental, and asset-level data, always on and always learning.

This is technical work that requires patience. It requires teams willing to operate at the intersection of AI research, geospatial science, distributed systems, and enterprise deployment. It is also incredibly rewarding. Join us at Neural Earth, the next frontier is here.

Neural Earth is seeking a skilled and inquisitive Geospatial Data Scientist to support the development of advanced geospatial analytics and machine learning capabilities at Neural. This role plays a key part in designing and delivering data products that address critical challenges in environmental monitoring, infrastructure risk, and spatial decision-making. You will collaborate with engineers, scientists, and product managers to build models that process satellite, aerial, and vector data, enabling clients to extract meaningful insights from complex spatial datasets.

Your day-to-day will involve building workflows to clean, analyze, and transform large-scale spatial and temporal datasets. You'll apply modern statistical and machine learning techniques to detect patterns, model trends, and support predictive analytics. Whether estimating climate impacts or classifying remote sensing images, your work will inform real-world decisions across industries. This position is ideal for individuals who love to work at the intersection of spatial intelligence and AI, are passionate about open data and tools, and thrive in collaborative, mission-driven environments.

• Develop machine learning models using geospatial data (e.g., satellite imagery, LiDAR, vector features).
• Implement data pipelines to handle large spatial datasets from multiple sensors and platforms.
• Perform exploratory data analysis (EDA) and visualization using modern geospatial tools.
• Apply supervised and unsupervised learning techniques for classification, segmentation, and prediction.
• Collaborate with engineers and product stakeholders to operationalize analytics in cloud environments.
• Document workflows, write technical summaries, and contribute to publications and reports.

• Bachelor's or Master's degree in Data Science, Remote Sensing, Environmental Science, Geography, or related field.
• 3+ years of experience applying machine learning to geospatial data.
• Proficiency in Python and geospatial libraries (e.g., Rasterio, GDAL, GeoPandas, Scikit-learn, PyTorch, TensorFlow).
• Familiarity with GIS tools like QGIS or ArcGIS. Experience with Mapbox, Leaflet, and Cesium. Experience in Python and Python notebooks.
• Experience with spatial databases (e.g., PostGIS, DuckDB, Timescale, MongoDB) and cloud-native workflows.
• Experience with search such as ELK stack, Meilisearch, or OpenSearch. Ability to document and articulate architecture diagrams and artifacts. Excellent written and verbal communication skills.

• Experience with graph databases such as ArangoDB or Neo4j a plus.
• Experience with multispectral imagery, SAR data, weather data, or environmental/climate modeling.
• Familiarity with Node.js, React.js, Vue.js. Familiarity with cloud platforms like AWS or Google Earth Engine.
• Prior contributions to open-source geospatial libraries or peer-reviewed research.
• Knowledge of Docker, Kubernetes, or CI/CD for ML pipelines.

The compensation range for this role is $135,000 - $200,000 annually, based on role, level, and expertise.