Learning Data Analysist

The Learning Data Analyst is responsible for converting data from various sources, including programs, research, and direct farmer experiences, into reliable and actionable insights. This process is crucial for strengthening evidence-based learning, guiding strategic decision-making, and facilitating flexible program delivery, ultimately leading to improved results for smallholder farmers and agricultural businesses.

1. Collaborate with program teams to convert key questions and hypotheses into practical analysis plans, defining users, methodologies, data requirements, and review schedules.
2. Evaluate existing evidence before suggesting new data collection. Assist in designing tools, sampling strategies, measurements, and field protocols, consulting specialists for intricate designs or impact evaluations.
3. Gather, clean, and organize survey, trial, enterprise, and qualitative data.
4. Manage data dictionaries, cleaning logs, and version-controlled analysis files. Resolve data inconsistencies with source owners, retain original records, and document any unresolved limitations before utilizing findings.
5. Assist field teams with data checks and practical enhancements to data collection instruments.
6. Perform descriptive, comparative, and suitable statistical analyses; identify trends and significant differences across regions, gender, age, farmer situations, or enterprise types, provided sample sizes and data quality are adequate.
7. Code and synthesize qualitative data from interviews, open responses, case studies, and reflection records. Integrate these with quantitative results to investigate underlying mechanisms, farmer decisions, and contextual variations.
8. Verify calculations, assumptions, and alternative explanations; report on uncertainties, selection bias, and other limitations.
9. Produce clear evidence summaries, charts, tables, analytical briefs, and presentations. Create or maintain dashboards only when they fulfill an agreed learning requirement and can be regularly updated.
10. Support program colleagues, farmers, and partners in interpreting findings, validating explanations against experience, and identifying areas for further investigation. Explain analyses in simple terms and document differing interpretations.
11. Contribute analytical insights and methodologies to research reports and publications.
12. Mentor colleagues on fundamental data checks, interpretation, chart reading, and common analytical errors using practical program work. Provide constructive responses to questions and clarify analytical decisions.
13. Organize approved evidence packages within the shared repository.

1. Hold a Bachelor’s degree in statistics, economics, agricultural economics, data science, agricultural sciences, or a related field, with significant quantitative training.
2. Possess at least 2 years of relevant work experience.
3. Have practical experience analyzing agricultural or rural development data, translating findings into program or research decisions, and understanding smallholder farming conditions.
4. Demonstrate proficiency in cleaning, managing, and analyzing quantitative datasets using Excel and at least one statistical or programming package (e.g., R, Stata, Python, or SPSS).
5. Experience with statistical reasoning, understanding sampling and measurement limitations, systematic qualitative analysis, and synthesizing diverse evidence sources.