Geospatial Data Scientist

Job Summary

We are seeking a highly skilled Geospatial Data Scientist to join our growing data science team. The ideal candidate will leverage geographic information systems (GIS), machine learning, spatial analytics, and big data technologies to extract meaningful insights from location-based datasets. This role will support business decisions related to logistics, transportation, urban planning, environmental analysis, retail site selection, and location intelligence.

The Geospatial Data Scientist will collaborate with data scientists, GIS specialists, software engineers, and business stakeholders to develop innovative geospatial solutions that improve operational efficiency and strategic planning.

Key Responsibilities

Spatial Data Analysis

• Analyze large-scale geospatial datasets from multiple sources, including GPS, satellite imagery, remote sensing data, demographic data, and IoT devices.
• Perform spatial clustering, hotspot analysis, route optimization, and location intelligence studies.
• Identify geographic trends, patterns, and anomalies to support business objectives.

Machine Learning & Predictive Modeling

• Develop predictive models using geospatial and temporal data.
• Build machine learning solutions for demand forecasting, site selection, risk assessment, and transportation optimization.
• Apply advanced statistical techniques to spatial datasets.

GIS & Mapping Solutions

• Create interactive maps, dashboards, and geospatial visualizations.
• Develop and maintain GIS databases and spatial data infrastructures.
• Integrate GIS solutions with enterprise applications and business intelligence platforms.

Data Engineering & Automation

• Design and maintain geospatial ETL pipelines.
• Automate spatial data processing workflows.
• Ensure data quality, consistency, and integrity across geospatial systems.

Cross-Functional Collaboration

• Work closely with business leaders to identify location-based opportunities and challenges.
• Translate complex geospatial findings into actionable business recommendations.
• Support strategic planning initiatives through data-driven insights.

Research & Innovation

• Evaluate emerging geospatial technologies and methodologies.
• Implement innovative solutions using AI, machine learning, and cloud-based GIS platforms.
• Stay current with advancements in remote sensing, satellite analytics, and location intelligence.

Required Qualifications

Education

• Bachelor’s degree in Data Science, Geography, Geographic Information Systems (GIS), Computer Science, Statistics, Environmental Science, or a related field.
• Master’s degree preferred.

Experience

• 3+ years of experience in geospatial analytics, GIS, or data science.
• Experience working with large geospatial datasets and spatial databases.
• Proven experience developing predictive models and data visualization solutions.

Technical Skills

Programming Languages

• Python
• SQL
• R (Preferred)

GIS & Geospatial Tools

• ArcGIS
• ArcGIS Pro
• QGIS
• PostGIS
• GeoPandas
• GDAL
• Shapely
• Google Earth Engine

Data Science & Machine Learning

• Scikit-learn
• TensorFlow
• PyTorch
• Statistical Modeling
• Predictive Analytics
• Time-Series Analysis

Database Technologies

• PostgreSQL/PostGIS
• MySQL
• Snowflake
• BigQuery

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Visualization Tools

• Tableau
• Power BI
• ArcGIS Dashboards
• Python Visualization Libraries

Preferred Qualifications

• Experience with satellite imagery and remote sensing technologies.
• Knowledge of transportation, logistics, urban planning, environmental science, or telecommunications industries.
• Experience with geospatial AI and computer vision applications.
• Familiarity with cloud-native geospatial architectures.

Key Competencies

• Spatial Analysis
• Critical Thinking
• Problem Solving
• Data Storytelling
• Statistical Analysis
• Geographic Information Systems
• Machine Learning
• Communication Skills
• Business Acumen
• Project Management

Typical Projects

• Retail store site selection optimization.
• Delivery route optimization and logistics planning.
• Smart city and urban planning initiatives.
• Environmental impact and climate risk assessment.
• Disaster response and emergency management analytics.
• Telecommunications network coverage analysis.
• Real estate market intelligence and location strategy.