Main duties and responsibilities include:

• Support the development/refining of standards-based digital tools to collect field trial and survey data.
• Use and further develop an existing workflow to annotate and upload standardized FAIR data to CGIAR repositories.
• Develop/refine existing scripts and tools for advanced statistical analysis that can augment, curate, aggregate, and analyze agronomic data.
• Utilize process-based and machine learning models for spatial and temporal crop yield prediction for decision support.
• Training of machine learning models using legacy agronomy data sets to generate hyperlocal fertilizer recommendations.
• Contribute to developing digital decision support tools to deliver tailored agronomic recommendations to partners and take part in testing and debugging of digital and analogue tools.
• Maintain manuals for decision-support tools and documentation on scripts, procedures, specifications, and reporting of relevant data sources (e.g., describing structure, format, and how to access and use them).
• Maintain excellent documentation of scripts, procedures, specifications and reporting of relevant data sources (e.g., describing structure, format and how to access and use them.

Requirements

Qualification and Experience

• Bachelor’s degree or higher in Data Science, Computer Science, Information Technology with experience in agriculture (preferred), or agriculture degree with strong data science experience.
• At least three (3) years of relevant work experience with key strengths in data management, analytics, and modelling.
• Experience using process-based crop (DSSAT, APSIM) or empirical (Machine learning) modelling tools is necessary.
• Advanced experience in programming (R and/or Python preferred).
• Experience in using collaborative tools and platforms for software development (e.g., Git and GitHub) and methods to maintain code integrity, documentation, and standards is an added advantage.
• Good communication skills and command of English.
• Excellent team player, self-motivated and independent problem solver.
• Interest in agricultural research.