Agronomy Data Officer (CropSense AI)

About CipherSense AI & CropSense AI CipherSense AI Technologies builds AI infrastructure for African markets. CropSense AI is our Earth Observation-powered agri-financial intelligence product, delivering risk and yield intelligence to financial institutions, MFIs, and DFIs operating across Africa's agricultural sector. CropSense combines satellite data with ground-truth agronomic knowledge to power YieldRank, our institutional risk-scoring engine, alongside farmer-facing tools that make that same intelligence useful on the ground. We're looking for the right candidate to help improve the regional context of our proprietary agronomic knowledge layer that sits underneath CropSense's models. The Role The Agronomy Data Officer is responsible for developing CropSense AI's proprietary library of agricultural best practices and agronomic data across multiple African regions. You'll translate real-world farming knowledge — planting calendars, input regimes, pest and disease risk patterns, soil-crop suitability — into structured, well-documented datasets that feed our risk models and farmer-facing tools. This is a hands-on, individual-contributor role. You won't be setting agronomic strategy — you'll be building the data foundation that strategy depends on, working closely with our data science and EO teams. What You'll Do Research and document region-specific agronomic best practices (planting windows, fertilizer/input regimes, pest and disease risk calendars, soil-crop suitability) across priority crops (maize, cassava, rice, cocoa, and others as we expand). Convert field and literature-based agronomic knowledge into clean, structured, and machine-readable data infrastructure (taxonomies, decision rules, scored risk factors) in collaboration with our data annotation team. Validate and sanity-check ground-truth agronomic assumptions against satellite-derived data outputs (e.g., NDVI, EVI, NDWI, soil moisture anomalies). Build and maintain relationships with extension officers, agricultural researchers, cooperatives, and agro-dealers to source and verify regional data. Identify agronomic variables likely to correlate with yield outcomes and credit risk, working with the product team to refine YieldRank's scoring logic. Maintain data quality, versioning, and documentation standards for all agronomic datasets produced. Travel periodically to priority regions for field validation and data collection. What We're Looking For Required: Degree in Agronomy, Agricultural Science, Crop Science, Soil Science, or a closely related field. 2–3 years of field or extension experience with African smallholder agriculture (research institution, NGO, agribusiness, or government extension background all qualify). Working knowledge of at least one of our priority crops (maize, cassava, rice, cocoa) in a specific regional context. Strong data literacy and organization skills — comfort transforming unstructured qualitative field reports or research papers into clean, tabular, and logic-driven frameworks (e.g., Excel/Google Sheets matrices). Strong plus: Exposure to remote sensing / EO concepts (even at a conceptual level — you don't need to build models, but should be able to discuss satellite-derived indicators with our technical team). Experience with data collection / annotation, taxonomy design, or structured knowledge bases. What Success Looks Like (First 6 Months) Comprehensive agronomic best-practice datasets delivered for at least 2–3 priority regions/crops. A working process for sourcing and validating regional agronomic data, including an initial network of regional contributors. Agronomic inputs integrated into at least one iteration of YieldRank's risk scoring logic. CropSense AI is a product of CipherSense AI Technologies Ltd.