Research Scientist at Strathmore University
Strathmore University
Job description
Job Summary The Research Scientist leads the statistical and predictive modelling core of Strathmore Agri-Food Innovation Center (SAFIC) Data Analysis & Market Intelligence pillar. The role applies mixed-effects and hierarchical models, time-series forecasting and machine learning to agricultural, economic and biological data (from farm, herd and trial records to national production, trade and price series) to produce decision-grade evidence for government, investors and agribusiness. The ideal candidate combines a strong statistical foundation with domain grounding in agriculture, livestock or agricultural economics, and contributes to data-driven solutions that enhance productivity, sustainability and innovation in Africa's agri-food systems. Job Details Design, fit and interpret mixed-effects, hierarchical and longitudinal models for structured agricultural data (repeated measures; nested farm/county/region effects; genetic, environmental and management variance components). Build predictive and forecasting models (time-series, panel regression, gradient boosting and ensemble methods) for production, demand, price and market-intelligence questions at a national and sub-national level. Develop population and value-chain projection models (herd dynamics, yield response, supply - demand balances) that feed policy, investment and sector-planning analyses. Lead the analytical design of data-analytics projects, producing insights that drive evidence-based policymaking and private-sector decisions. Collaborate with government, research partners and industry stakeholders to frame analytical questions and resolve data challenges in agriculture. Contribute statistical models and outputs to digital tools and dashboards developed with the data engineering team. Prepare technical reports, peer-reviewed publications and visualizations that communicate findings to diverse audiences. Ensure adherence to data governance standards, ethical AI principles and best practices in reproducible data management. Work closely with the pillar lead to refine methodologies, improve model performance and scale analytics solutions. Requirements Minimum Requirements Master's or PhD in Statistical/Quantitative Genetics, Crop or Animal Breeding, Agricultural Economics, Biostatistics, Statistics or a closely related quantitative field. Candidates with a Data Science or Computer Science background will be considered only with demonstrated applied experience in agricultural or biological research and data analytics. Demonstrated expertise in mixed models (e.g. lme4/nlme, ASReml, SAS PROC MIXED or equivalent), generalised linear models, and predictive/forecasting methods. Strong expertise in machine learning and predictive analytics, with sound judgement on when statistical inference versus algorithmic prediction is appropriate. 3+ years' experience in applied statistical analysis or quantitative research. 3+ years' experience in agricultural data collection, management and analysis (livestock, crop, farm-survey, market or trade data). Evidence of applied output: peer-reviewed publications, technical reports or models that have informed real policy, investment or operational decisions. Proficiency in R, STATA, SAS and/or Python for statistical modelling; familiarity with dashboard tools (e.g. Power BI, Tableau) is an advantage. Demonstrated ability to work with large, messy, multi-source datasets and derive actionable insights. Desirable (added advantage) Breeding-value estimation, genomic prediction or variance-component estimation in livestock or crops. Agricultural economics modelling: partial-equilibrium or CGE models, supply-response or demand-system estimation. Bayesian methods (e.g. Stan, INLA, brms) and spatial or geospatial statistics. Experience with remote-sensing or GIS data in agricultural applications. Experience working with government or development-partner data systems in Africa. Competencies and Attributes Strong statistical foundation (experimental design, inference and model diagnostics) alongside a sound understanding of AI/ML techniques. Solid background in biological or agricultural sciences, with the ability to frame agricultural questions as statistical problems. Ability to translate analytical outputs into clear, user-friendly insights for policy and business audiences. Strong problem-solving skills and analytical thinking. Effective collaboration skills and ability to work in multidisciplinary teams. Excellent communication skills for both technical and non-technical audiences. Commitment to reproducible, ethical data use and to agricultural transformation.
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