How your day-to-day will look like:

Technical & Geospatial

• You apply VIDA’s spatial analysis tools and prepare our software for real-life projects - these can range from energy access planning to agriculture or climate risk assessment.
• You improve, automate, and extend our workflows with Python and SQL scripting.
• You explore, pre-process and integrate new datasets and map layers into the VIDA software.
• You explore and implement new spatial data analysis methods, for example by using Machine Learning methods and sector-specific modelling frameworks.

Documentation, Reporting & Communication

• You ensure clear documentation, reporting, and communication across all project phases.
• You ensure project outputs (VIDA software products and reports) meet technical quality standards and are accessible to end-users.
• You interact with our international project partners and clients, and prepare/present your work in reports and presentations, both in English and French.
• You facilitate workshops, trainings, and meetings to ensure alignment and adoption of project outcomes, both in English and French.

Is it you we’re looking for?

• You have a Master’s degree (M.Sc.) or equivalent qualification in energy / engineering / science with a focus on spatial data analysis.
• Minimum 3-5 years of professional experience in geospatial data analyses with knowledge of data structures, algorithms, tools, and the fundamentals of statistics and mathematics underlying data science.
• Minimum 1-2 years of professional experience in energy access projects and with energy sector planning tools (e.g., least-cost electrification models, demand forecasting).
• Proficiency in Python for geospatial data analysis (e.g. geopandas, shapely, rasterio, gdal).
• Experience with Geographic Information Systems (for example QGIS, ArcGIS).
• Excellent communication skills. You truly enjoy presenting your work to clients, engaging with them, and being part of project-delivery teams.
• Experience with writing technical reports around geospatial data analyses.
• Fluency in French (oral and written), as much as English.
• Comfort and initiative in adopting AI tools (e.g., for coding, analysis, or documentation) as part of daily workflows to enhance efficiency and innovation.