Learning Data Analysist at FIPS Africa

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.

Interpretation and accessible communication

• 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.
• 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.
• Contribute analysis and methods to research reports and publications

Analytical capability and knowledge continuity

• 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.
• Organise approved evidence packages in the shared repository

Qualification & Experience.

• A bachelor’s degree in statistics, economics, agricultural economics, data science, agricultural sciences or related discipline, with substantial quantitative training,
• 2 years work experience
• Practical experience analyzing agricultural or rural-development data and translating findings into programme or research decisions, with an understanding of smallholder farming conditions.
• 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.
• Experience with statistical reasoning, sampling and measurement limitations, systematic qualitative analysis and synthesis of different evidence sources.