Job Highlight:

Reporting to the Data Lab Data Science Lead, with a dotted line to a Senior Scientist in Land & Carbon Lab, you will contribute to the research, innovation, implementation and development of new data analysis technologies that leverage artificial intelligence to detect and map crop-field boundaries and identify crop-types and related features with satellite data. You will be supported by a team of data scientists from WRI Data Lab, spatial analysts from WRI Land and Carbon Lab, and supply chain and land use policy experts from our Global and Africa teams.

What will you do:

Spatial Analysis with AI Tools for Agriculture Use Cases (65%):

• Create an open AI model for crop field boundary detection with satellite images that can be used by actors across supply chains and sectors
• Evaluate data products for completion and accuracy, with manual and automated methods
• Evaluate sources of data ranging from imagery to field data to develop effective and efficient modeling strategies
• Assist in crop-type/yield mapping and other mapping endeavors with your AI expertise

Program Development and Administration (10%):

• Contribute to concept notes, funding proposals, and donor reports
• Support development of workplans and related internal systems and procedures to ensure timely execution of activities and delivery of outputs
• Liaise with external partners and WRI colleagues across programs and countries ·
• Manage internal communications on project progress from the project team to a wider internal stakeholder group

Writing and Engagement (25%):

• Contribute to written outputs such as peer-reviewed publications, technical notes, and presentations
• Assist in the development of documentation and standardization of code and data products
• Assist with data-specific outreach and communication from private-sector, government, and non-profit partner organizations, including conducting authoring technical write-ups for one-off analyses
• Assist with the design and coordination of engagement activities, capacity building and other exercises to accelerate uptake of data products