About this role
Learning Data Analysist
- Job Type Full Time
- Qualification BA/BSc/HND
- Experience 2 years
- Location Nairobi
- Job Field Data, Business Analysis and AI , ICT / Computer
Role Purpose of the Role
The Learning Data Analyst transforms programme, research, and farmer-experience data into credible, actionable insights that strengthen evidence-based learning, inform decision-making, and support adaptive programme delivery to improve outcomes for smallholder farmers and agricultural enterprises.
Key responsibilities
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Learning questions and analytical design
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Work with programme teams to turn priority questions and hypotheses into feasible analysis plans with identified users, methods, data needs and review dates.
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Assess existing evidence before proposing new collection. Support the design of tools, sampling, measures and field protocols, seeking specialist input for complex designs or impact evaluation.
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Data preparation and quality
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Compile, clean, survey, trial, enterprise and qualitative data.
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Maintain data dictionaries, cleaning logs and versioned analysis files. Reconcile discrepancies with source owners, preserve original records and document unresolved limitations before findings are used.
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Support field-team checks and practical improvements to collection tools.
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Quantitative and qualitative analysis
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Undertake descriptive, comparative and appropriate statistical analysis; analyse trends and relevant differences by region, gender, age, farmer circumstances or enterprise type where sample sizes and data quality permit.
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Code and synthesise interviews, open responses, case studies and reflection records. Combine these with quantitative findings to explore mechanisms, farmer choices and contextual differences.
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Check calculations, assumptions and alternative explanations; report uncertainty, selection bias and other limits.
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Interpretation and accessible communication
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Prepare clear evidence summaries, charts, tables, analytical briefs and presentations. Develop or maintain dashboards only where these serve an agreed learning need and can be kept current.
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Help programme colleagues, farmers and partners interpret findings, test whether explanations fit experience and identify what still needs investigation. Explain the analysis in plain language and record competing interpretations.
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Contribute analysis and methods to research reports and publications
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Analytical capability and knowledge continuity
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Coach colleagues on basic data checks, interpretation, chart reading and common analytical errors through real programme work. Respond constructively to questions and make analytical choices understandable.
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Organise approved evidence packages in the shared repository
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Qualification & Experience.
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A bachelor’s degree in statistics, economics, agricultural economics, data science, agricultural sciences or related discipline, with substantial quantitative training,
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2 years work experience
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Practical experience analyzing agricultural or rural-development data and translating findings into programme or research decisions, with an understanding of smallholder farming conditions.
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Demonstrated ability to clean, manage and analyze quantitative datasets using Excel and at least one statistical or programming package such as R, Stata, Python or SPSS.
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Experience with statistical reasoning, sampling and measurement limitations, systematic qualitative analysis and synthesis of different evidence sources.
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