Learning Data Analysist

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.