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

The Learning officer makes evidence of a routine part of how FIPS plans, delivers and adapts its programmes. The role helps teams ask decision-relevant questions, generate and interpret credible evidence, and act on what they learn within the agricultural season, not after it.

Strategic contribution

• Sharper decisions: management and programme teams can see what the evidence supports, what remains uncertain and what it would take to find out.
• Faster adaptation: learning is timed to seasons and review points so that findings change delivery while it still matters.
• Stronger positioning: credible, well-documented evidence and publications strengthen FIPS with research partners, funders and the wider sector.
• Stronger capability: colleagues build their own inquiry and reflection skills, so learning does not depend on one post.

Key responsibilities

Learning agenda and strategic questions

• Co-develop a manageable learning agenda with programme and MELA colleagues, linking each priority question to a strategic decision, programme assumption or evidence gap.
• Help teams make theories of change explicit: what is expected to change, for whom, through which mechanism, and what evidence would support or challenge that expectation.
• Apply consistent lenses across questions where relevant: differences by gender, age and farm type; climate and seasonal risk; and the sustainability of enterprise and market relationships beyond FIPS support.
• Translate agreed priorities into feasible learning plans with decision users, methods, responsibilities, resources and review points aligned with seasons. Propose sequencing and flag constraints for approval.

Applied learning and analysis

• Help teams choose methods that fit the question, context, time and resources, including farmer-led experimentation, on-farm trials, participatory inquiry, case studies and mixed-method studies.
• Design and undertake agreed studies, including protocols, sampling, field tools, fieldwork supervision and data-quality checks, with programme teams, MELA colleagues and technical specialists.
• Apply informed consent, confidentiality, responsible data handling and safeguarding requirements. Identify when research approvals are needed and work through the authorised process before fieldwork.
• Analyse quantitative and qualitative evidence with appropriate tools. Examine differences across farmer and enterprise groups where the data permit; assess bias, missing data and uncertainty; and avoid causal claims the design cannot support.
• Use digital data collection, analysis and AI-assisted tools where they add rigour or speed, with human review of outputs, protection of personal data and transparency about how tools were used.
• Validate findings with farmers, programme colleagues and research partners. Record divergent views, unexpected results and open questions, and keep a traceable link from data to conclusions.

• Co-develop a manageable learning agenda with programme and MELA colleagues, linking each priority question to a strategic decision, programme assumption or evidence gap.
• Help teams make theories of change explicit: what is expected to change, for whom, through which mechanism, and what evidence would support or challenge that expectation.
• Apply consistent lenses across questions where relevant: differences by gender, age and farm type; climate and seasonal risk; and the sustainability of enterprise and market relationships beyond FIPS support.
• Translate agreed priorities into feasible learning plans with decision users, methods, responsibilities, resources and review points aligned with seasons. Propose sequencing and flag constraints for approval.
• Help teams choose methods that fit the question, context, time and resources, including farmer-led experimentation, on-farm trials, participatory inquiry, case studies and mixed-method studies.
• Design and undertake agreed studies, including protocols, sampling, field tools, fieldwork supervision and data-quality checks, with programme teams, MELA colleagues and technical specialists.
• Apply informed consent, confidentiality, responsible data handling and safeguarding requirements. Identify when research approvals are needed and work through the authorised process before fieldwork.
• Analyse quantitative and qualitative evidence with appropriate tools. Examine differences across farmer and enterprise groups where the data permit; assess bias, missing data and uncertainty; and avoid causal claims the design cannot support.
• Use digital data collection, analysis and AI-assisted tools where they add rigour or speed, with human review of outputs, protection of personal data and transparency about how tools were used.
• Validate findings with farmers, programme colleagues and research partners. Record divergent views, unexpected results and open questions, and keep a traceable link from data to conclusions.

• Data analysis
• Quantitative and qualitative research methods
• Digital data collection tools
• AI-assisted tools
• Informed consent
• Confidentiality
• Responsible data handling
• Safeguarding
• Research approvals process
• Farmer engagement
• Programme adaptation
• Evidence generation and interpretation

• BA/BSc/HND