Research Scientist
AI summary
Strathmore University is hiring a Research Scientist to lead the statistical and predictive modelling core of the Strathmore Agri-Food Innovation Center (SAFIC) Data Analysis & Market Intelligence pillar. The role applies mixed-effects, hierarchical, time-series and machine learning models to agricultural, economic and biological data to produce decision-grade evidence for government, investors and agribusiness. A relevant Master’s or PhD and at least 3 years of research experience are required.
- Leads statistical and predictive modelling for SAFIC’s Data Analysis & Market Intelligence pillar
- Applies mixed-effects, hierarchical, time-series and ML models to agricultural and economic data
- Produces evidence for government, investors and agri-business decision-making
- Requires a relevant Master’s or PhD and 3+ years research experience
- Full-time role based in Nairobi with a deadline of 09/10/2026
AI job guide
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AI salary guide
Source salary availableThe source lists Open. Confirm the final pay, benefits, contract terms and allowances directly with the employer before accepting an offer.
Can you qualify for this role?
- Required3+ years of relevant experienceThe job post includes a minimum experience signal.
- PreferredPractical evidence in research, modelling, analysisThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in Not specifiedThe vacancy is associated with this location.
- UnclearComfort with the Full Time contract termsConfirm hours, duration, probation and benefits at the original source.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Research Scientist evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to Strathmore University and the role in Not specified.
- Add concrete examples related to research, modelling, analysis, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from Corporate Staffing Kenya; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Plan to submit before the listed deadline: 2026-10-09.
Source and safety check
- Corporate Staffing Kenya
- Original source link available
- Application method is clear
- Deadline is available: 2026-10-09
- No major risk signal was detected in the captured text.
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Interview preparation
- What experience makes you a strong fit for this Research Scientist role in research, modelling?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Not specified under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research Strathmore University before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
Job Title: Research Scientist
Date Posted: 30/09/2026
Job Type: Full Time
Job Level: Middle
Employer: Strathmore University
Industry: Project Management
Salary: Open
Location: Nairobi
Country: Kenya
Deadline: 09/10/2026
Project Management Jobs, Strathmore University Jobs. Research Scientist conducts academic research, secures grant funding, and publishes scholarly articles, requires a relevant Master’s or PhD and 3+ years research experience.
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.
Key Accountabilities
- 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.