In A Nutshell
Lead on spatial analysis and modeling for the alliance include overseeing data collection and management.
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.