Learning Data Analyst
2026-10-29T14:53:37+00:00
FIPS Africa
https://cdn.greatkenyanjobs.com/jsjobsdata/data/employer/comp_12850/logo/download.png
https://fipsafrica.org/
FULL_TIME
Nairobi
Nairobi
00100
Kenya
Nonprofit, and NGO
Data, Business Analysis and AI, ICT / Computer, Internships, Agribusiness, Agricultural Services & Products
2026-11-11T17:00:00+00:00
8
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.
JOB-6ae35df13ae06
Vacancy title:
Learning Data Analyst
[Type: FULL_TIME, Industry: Nonprofit, and NGO, Category: Data, Business Analysis and AI, ICT / Computer, Internships, Agribusiness, Agricultural Services & Products]
Jobs at:
FIPS Africa
Deadline of this Job:
Wednesday, November 11 2026
Duty Station:
Nairobi | Nairobi
Summary
Date Posted: Thursday, October 29 2026, Base Salary: Not Disclosed
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JOB DETAILS:
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.
Work Hours: 8
Experience in Months: 24
Level of Education: bachelor degree
Job application procedure
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