Director, Spatial Data Science

Key Responsibilties

• Set the scientific vision for how satellite, UAV, ground, and IoT sensor data converge into a unified spatial analytics fabric that scales globally
• Own remote-sensing data strategy, negotiate provider agreements, define data acquisition cadences and sources, and manage petabyte-scale archives in cloud object stores and spatio-temporal databases
• Lead the design, development, and deployment of ML & AI models focused on feature extraction from 2D and 3D data (lidar and imagery), using our ML CI/CD workflow as well as contributing to development
• Drive research and development efforts to enhance models' accuracy, efficiency, and scalability in a production environment
• Collaborate with cross-functional teams, including software developers, product managers, and subject-matter experts in agriculture and forestry, to identify customer pain points and solution opportunities
• Oversee data acquisition and preprocessing pipelines, ensuring data quality and integrity for machine learning models
• Drive IP creation and publication: prototype novel algorithms, file patents, and present at leading geospatial conferences
• Establish data-quality & ethics governance uncertainty quantification, bias monitoring, and compliance with geospatial privacy regulations
• Mentor and grow a high-performing team; foster a culture of experimentation, peer review, and continuous learning
• Partner externally with universities, space agencies, and analytics vendors to co-develop models, share datasets, and unlock co-selling opportunities

Qualifications

• 10+ years applying remote sensing and geospatial analytics in production environments; 5+ years leading technical teams
• Deep expertise in satellite & aerial imagery, LiDAR point clouds, SAR, multispectral/hyperspectral data, and their preprocessing workflows
• Strong experience with tools and libraries such as TensorFlow, PyTorch, SAM, DINO, OpenCV, and other machine learning frameworks
• Experience working with 2D and 3D data sources such as lidar, camera imagery, and point clouds.
• Familiarity with geospatial data processing, GIS, Photogrammetry, Neural Radiance Fields, Gaussian Splatting, and remote sensing data analytics
• Solid understanding of agriculture, forestry or natural asset management applications is a plus.
• Strong communication and leadership skills, with the ability to articulate complex technical concepts to non-technical stakeholders
• Proven track record delivering cloud-native pipelines on AWS, Azure, or GCP using Spark, Dask, Kubernetes, or serverless architectures
• Strong programming chops in Python (plus one of: C++, Rust, or Go) and experience with modern MLOps stacks (MLflow, Metaflow, Vertex AI, or similar)
• Exceptional communication skills: able to explain NDVI and graph attention networks to agronomists, executives, and engineers alike
• Experience fusing in-situ IoT sensor data with satellite/UAV imagery
• Bachelor’s or Master’s in Remote Sensing, Geomatics, Computer Science, or a related quantitative field; PhD a plus