Spatial Data Scientist

Responsibilities

Data Collection and Management

• Data Acquisition: Gather spatial data from diverse sources such as satellite imagery, GPS data, remote sensing technologies, and public databases.
• Data Cleaning and Preprocessing: Clean and preprocess spatial data to ensure accuracy, consistency, and usability. This includes handling missing data, correcting errors, and standardizing formats.
• Data Storage and Management: Design and manage spatial databases and data warehouses, ensuring efficient storage, retrieval, and management of large volumes of spatial data.

Spatial Analysis and Modeling

• Geospatial Analysis: Perform geospatial analyses such as buffer analysis, overlay analysis, and spatial statistics to extract meaningful patterns and trends from spatial data.
• Predictive Modeling: Develop and implement predictive models using spatial data to forecast trends and outcomes. This may include land use change modeling, environmental impact assessments, and urban growth modeling.
• Machine Learning: Apply machine learning algorithms to spatial data for tasks such as image classification, object detection, and spatial clustering.

Data Visualization and Reporting

• Visualization: Create detailed maps, charts, and interactive visualizations using GIS (Geographic Information Systems) and other data visualization tools to effectively communicate spatial data insights to stakeholders.
• Reporting: Prepare comprehensive reports and presentations summarizing the results of spatial analyses and providing actionable insights and recommendations.
• Dashboard Development: Develop interactive dashboards for real-time monitoring and reporting of spatial data and analyses.

Application Development

• GIS Application Development: Develop custom GIS applications and tools to facilitate spatial data analysis and visualization for specific projects or organizational needs.
• Integration with Other Systems: Integrate spatial data and GIS applications with other organizational systems and databases to enhance data accessibility and utility.

Research and Development

• Innovation in Methods: Conduct research to develop new methods and techniques for spatial data analysis and modeling.
• Technology Assessment: Evaluate and adopt new tools and technologies in the field of spatial data science to enhance analytical capabilities.

Collaboration and Support

• Cross-functional Collaboration: Work closely with cross-functional teams, including program managers, data scientists, engineers, and policy makers, to understand their spatial data needs and provide tailored solutions.
• Technical Support and Training: Provide technical support and training to team members and stakeholders on the use of spatial data tools and methodologies.
• Fundraising: Participate in developing technical grant proposals

Skillset

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related fields.
  Some experience around geospatial platform development.
• Experience on GIS Application Development.
• Experience (not mandatory) on GEE application development.
• Knowledge on PowerBI.
• Knowledge of version control systems (e.g., Git).
• Familiarity with browser testing and debugging.
• Knowledge of SEO principles.
• Excellent interpersonal and communication skills.
• Strong problem-solving abilities and attention to detail.
• Ability to work independently and as part of a diverse, multicultural team.
• Bilingual proficiency in English is requisite (able to write and deliver conferences, reports, etc in both languages). French, Arabic or Spanish are highly desirable.
• A self-starter, disciplined, driven, eager to learn, grow, and make an impact.
• Experience (not mandatory) with Google Earth Engine (GEE) for geospatial and satellite data processing.
• Proficiency with geospatial libraries and frameworks such as GeoPandas, Rasterio, Shapely, xarray, rioxarray, GDAL/OGR, Leaflet, Mapbox GL, or OpenLayers.
• Experience working with different types of geospatial data (vector, raster, satellite, mobile, social media, administrative, census) and integrating multiple data sources.
• Strong analytical skills, including statistical/econometric modeling and machine learning (e.g., regression, classification, clustering, small-area estimation, spatial/temporal modeling) using frameworks like scikit-learn, statsmodels.
• Experience building interactive dashboards and analytical products using Power BI, Tableau, or web frameworks (Plotly/Dash, Streamlit, Bokeh, D3.js), with a focus on spatial data visualization.
• Familiarity with basic DevOps practices (e.g., testing, CI/CD, Docker is a plus) and documentation for reproducibility.