Apply appropriate bias-correction techniques to datasets using available in-situ observations.
Validate corrected datasets against ground-based observations.
Document methodologies, assumptions, limitations, and validation results.
Deliver processed datasets and a final technical report summarizing methods, results, and recommendations.
Compile and preprocess required datasets for continental-scale SWAG analysis using L-WRSI and SPAM dataset approaches.
Generate reproducible analytical outputs, maps, and datasets suitable for integration into DIWASA project platforms.
Prepare technical reports documenting methodologies, results, and policy-relevant insights.
Requirements
Master’s degree (or advanced-stage PhD) in Hydrology, Water Resources Engineering, Environmental Engineering, Earth Sciences, Geospatial Sciences, Data Science, or a related field.
Demonstrated experience in processing and analysis of hydro-climatological datasets, including remote sensing–based precipitation and/or modeled discharge data.
Experience applying bias-correction and validation techniques using in-situ observations.
Experience working with large spatial and temporal datasets.
Experience with cloud computing environments for data processing.
Experience using programming languages commonly applied in hydrology and geospatial analysis (e.g., Python, R).
Strong analytical and quantitative skills.
Ability to develop, document, and maintain reproducible analytical workflows and code.
Ability to work independently and manage technical tasks within agreed timelines.
Strong written communication skills for preparation of technical reports and documentation.
Experience with visualization and mapping of large-scale hydrological and geospatial datasets.
Tech Stack
Cloud
Python
Remote Sensing
Benefits
IWMI offers a competitive monthly rate for this assignment.
Duration of the contract will be for a period of nine (09) months.