Research Scientist

Strathmore University is an institution of higher education founded in 1961, located in Nairobi, Kenya, and is known for its focus on education and training.

This role involves applying mixed-effects and hierarchical models, time-series forecasting, and machine learning techniques to various agricultural, economic, and biological datasets. The objective is to generate evidence-based insights for government bodies, investors, and agribusinesses. The ideal candidate should possess a strong statistical background combined with expertise in agriculture, livestock, or agricultural economics, contributing to data-driven solutions that enhance productivity, sustainability, and innovation within Africa's agri-food systems.

1. Design, fit, and interpret mixed-effects, hierarchical, and longitudinal models for structured agricultural data.
2. Build predictive and forecasting models for production, demand, price, and market intelligence questions at national and sub-national levels.
3. Develop population and value-chain projection models, such as herd dynamics and yield response, to inform policy, investment, and sector planning analyses.
4. Lead the analytical design of data analytics projects, generating insights that support evidence-based policymaking and private-sector decisions.
5. Collaborate with government, research partners, and industry stakeholders to define analytical questions and resolve data challenges in agriculture.
6. Contribute statistical models and outputs to digital tools and dashboards developed with the data engineering team.
7. Prepare technical reports, peer-reviewed publications, and visualizations to effectively communicate findings to diverse audiences.
8. Ensure adherence to data governance standards, ethical AI principles, and best practices in reproducible data management.
9. Work closely with the pillar lead to refine methodologies, improve model performance, and scale analytics solutions.

1. Master’s or PhD in Statistical/Quantitative Genetics, Crop or Animal Breeding, Agricultural Economics, Biostatistics, Statistics, or a closely related quantitative field.
2. Candidates with a Data Science or Computer Science background will be considered if they have demonstrated applied experience in agricultural or biological research and data analytics.
3. Demonstrated expertise in mixed models (e.g., lme4/nlme, ASReml, SAS PROC MIXED or equivalent), generalized linear models, and predictive/forecasting methods.
4. Strong expertise in machine learning and predictive analytics, with sound judgment on when statistical inference versus algorithmic prediction is appropriate.
5. At least 3 years of experience in applied statistical analysis or quantitative research.
6. At least 3 years of experience in agricultural data collection, management, and analysis (e.g., livestock, crop, farm-survey, market, or trade data).
7. Evidence of applied output, such as peer-reviewed publications, technical reports, or models that have informed real policy, investment, or operational decisions.
8. Proficiency in R, STATA, SAS, and/or Python for statistical modeling; familiarity with dashboard tools (e.g., Power BI, Tableau) is an advantage.
9. Demonstrated ability to work with large, complex, multi-source datasets and derive actionable insights.
10. Strong statistical foundation encompassing experimental design, inference, and model diagnostics, alongside a solid understanding of AI/ML techniques.
11. Solid background in biological or agricultural sciences, with the ability to translate agricultural questions into statistical problems.
12. Ability to translate analytical outputs into clear, user-friendly insights for policy and business audiences.
13. Strong problem-solving skills and analytical thinking.
14. Effective collaboration skills and ability to work in multidisciplinary teams.
15. Excellent communication skills for both technical and non-technical audiences.
16. Commitment to reproducible, ethical data use and to agricultural transformation.
17. *Desirable:* Experience with breeding-value estimation, genomic prediction, or variance-component estimation in livestock or crops.
18. *Desirable:* Experience with agricultural economics modeling, including partial-equilibrium or CGE models, supply-response, or demand-system estimation.
19. *Desirable:* Familiarity with Bayesian methods (e.g., Stan, INLA, brms) and spatial or geospatial statistics.
20. *Desirable:* Experience with remote-sensing or GIS data in agricultural applications.
21. *Desirable:* Experience working with government or development-partner data systems in Africa.