Sophomore Data Scientist Geospatial/Geospatial Machine Learning Scientist

The Sophomore Data Scientist Geospatial supports the design, development, and delivery of analytical and modeling solutions using geospatial data. This role works closely with senior data scientists, GIS teams, engineers, and business stakeholders to analyze complex datasets, develop spatial analytics solutions, and translate geospatial information into actionable business insights.

The position provides hands-on opportunities to develop scalable GIS tools, analytical applications, spatial data products, and reusable geoprocessing capabilities while gaining experience with enterprise geospatial platforms, cloud technologies, and AI/ML services.

Key Responsibilities

• Support end-to-end data science and geospatial analytics projects, including data preparation, exploration, feature engineering, modeling, testing, and documentation.
• Develop analytical and machine learning solutions using structured, unstructured, and geospatial datasets.
• Build and maintain spatial data pipelines, APIs, automation workflows, and geoprocessing tools.
• Develop reusable GIS tools, analytical applications, and spatial data products to support business and operational requirements.
• Perform spatial analysis, data visualization, statistical analysis, and predictive modeling.
• Develop production-ready Python-based analytical workflows and applications under the guidance of senior technical staff.
• Assist with integrating GIS capabilities with cloud platforms, enterprise data systems, APIs, and AI/ML services.
• Support the development and optimization of spatial data processing and analytical workflows.
• Participate in model validation, testing, monitoring, and performance optimization.
• Collaborate with GIS professionals, data engineers, software engineers, and business stakeholders.
• Document analytical methodologies, technical solutions, workflows, and results.
• Communicate analytical findings and recommendations to both technical and non-technical stakeholders.
• Learn and apply enterprise development standards, geospatial best practices, and modern data science methodologies.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Geography, GIS, Engineering, or a related quantitative field; advanced degree preferred.
• 2 4 years of professional experience in data science, analytics, machine learning, GIS, or a related field.
• Experience applying data science techniques to GIS or geospatial problems preferred, particularly within the last 4 years.
• Strong proficiency in Python and experience developing analytical or data processing workflows.
• Working knowledge of SQL and experience working with large or complex datasets.
• Understanding of statistical analysis, machine learning, predictive modeling, and data visualization.
• Hands-on experience with geospatial data, spatial analytics, or GIS technologies.
• Ability to develop analytical tools, applications, scripts, and automated workflows.
• Strong problem-solving, analytical, communication, and collaboration skills.