Geospatial Data Scientist II
Geospatial Analysis and Workflow Implementation
Implement established geospatial data science and remote sensing workflows from data acquisition through processing, analysis, model execution, validation, and delivery.
Acquire, organize, clean, and evaluate raster, vector, tabular, terrain, and Earth observation datasets from public, partner, and client sources.
Use Python, GIS software, and open-source tools to process large geospatial datasets and generate repeatable analytical outputs.
Run geospatial machine learning or deep learning workflows, evaluate model outputs, and identify important performance issues or data limitations.
Exercise judgment about the best available data, important caveats, and technical issues that should be elevated to senior technical staff.
Validation, Documentation, and Quality
Conduct spatial statistics, accuracy assessments, quality-control checks, and validation using established methods.
Prepare clear documentation of data sources, processing steps, code, assumptions, limitations, and results.
Review outputs for completeness, consistency, and technical quality before they are shared with partners or clients.
Contribute to reproducible project structures, data dictionaries, metadata, and technical handoff materials.
Automation and Process Improvement
Identify repetitive or error-prone steps that can be automated, standardized, or made more efficient.
Develop and maintain scripts, functions, notebooks, and reusable components that improve delivery speed and consistency.
Work with senior data scientists, AI engineers, and cloud engineers to move useful prototypes toward scalable workflows.
Test new datasets and methods and share practical findings with the broader technical team.
Collaboration and Delivery
Work closely with senior technical staff, AI engineers, GIS professionals, project managers, and subject-matter experts.
Communicate progress, technical considerations, results, and risks clearly in team meetings, written updates, and presentations.
Contribute to client deliverables, proposals, technical memos, presentations, and demonstrations.
Participate in peer review and contribute to an inclusive team culture focused on learning, quality, and impact.