Africas 33 million smallholder farmers feed families, power local economies, and hold the key to national food security. Yet too many remain underserved, achieving only a fraction of their potential as climate shocks intensify and food import bills rise. Hunger, poverty, and youth underemployment persist alongside fertile land and untapped opportunity. FI...

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

Learning questions and analytical design

• Work with programme teams to turn priority questions and hypotheses into feasible analysis plans with identified users, methods, data needs and review dates.
• 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.

Data preparation and quality

• Compile, clean, survey, trial, enterprise and qualitative data.
• 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.
• Support field-team checks and practical improvements to collection tools.

Quantitative and qualitative analysis

• 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.
• Code and synthesise interviews, open responses, case studies and reflection records. Combine these with quantitative findings to explore mechanisms, farmer choices and contextual differences.
• Check calculations, assumptions and alternative explanations; report uncertainty, selection bias and other limits.

• Work with programme teams to turn priority questions and hypotheses into feasible analysis plans with identified users, methods, data needs and review dates.
• 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.
• Compile, clean, survey, trial, enterprise and qualitative data.
• 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.
• Support field-team checks and practical improvements to collection tools.
• 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.
• Code and synthesise interviews, open responses, case studies and reflection records. Combine these with quantitative findings to explore mechanisms, farmer choices and contextual differences.
• Check calculations, assumptions and alternative explanations; report uncertainty, selection bias and other limits.

• BA/BSc/HND