Research Scientist – Agricultural Data Modeling

About the role

The Research Scientist will lead the statistical and predictive‑modelling core of the Strathmore Agri‑Food Innovation Center’s Data Analysis & Market Intelligence pillar. The role focuses on applying mixed‑effects, hierarchical and machine‑learning techniques to agricultural, economic and biological datasets to generate decision‑grade evidence for governments, investors and agribusinesses.

Key responsibilities

• Design, fit and interpret mixed‑effects, hierarchical and longitudinal models for structured agricultural data.
• Build predictive and forecasting models (time‑series, panel regression, gradient‑boosting, ensemble methods) for production, demand, price and market‑intelligence questions.
• Develop population and value‑chain projection models that feed policy, investment and sector‑planning analyses.
• Lead analytical design of data‑analytics projects and collaborate with government, research partners and industry stakeholders.
• Contribute statistical models to digital tools and dashboards and prepare technical reports, peer‑reviewed publications and visualisations.

Required profile

• Master’s or PhD in Statistics, Biostatistics, Agricultural Economics, Quantitative Genetics or a closely related quantitative field.
• At least 3 years of applied statistical analysis or quantitative research experience, including agricultural data collection and analysis.
• Demonstrated expertise in mixed models (e.g., lme4, nlme, ASReml, SAS PROC MIXED) and predictive/forecasting methods.
• Evidence of peer‑reviewed publications or technical reports that have informed policy or investment decisions.

Required skills

• Statistical programming in R, Python, STATA and SAS.
• Mixed‑effects and hierarchical modelling (lme4, nlme, ASReml, SAS PROC MIXED).
• Machine‑learning techniques and predictive analytics (gradient boosting, ensemble methods).
• Time‑series forecasting and panel regression.
• Dashboard development tools such as Power BI or Tableau.