MEL & Research Manager — Digital Green
Digital Green · Nairobi · Onsite
Job Description
Digital Green is hiring an MEL & Research Manager in Nairobi to support research, monitoring, evaluation and learning.
The role focuses on FarmerChat, its AI-powered agricultural advisory platform, across Kenya and Nigeria. It suits a researcher with strong quantitative and qualitative skills who can connect farmer research with digital product data.
The position also offers exposure to emerging AI-enabled research methods and opportunities to take increasing ownership of evaluation work.
About the Role
This is a hands-on research and analytics position at the intersection of agriculture, technology and AI. The successful candidate will investigate how farmers use FarmerChat and whether its advice influences decisions and outcomes.
Much of the work involves designing studies, analysing survey and product data, coordinating field research and translating findings into practical recommendations. The role reports to the Director of MEL and works across program, product, user research and technology teams.
Key Responsibilities
The role covers five main areas:
- Design MEL studies, surveys, qualitative research and evaluation approaches.
- Analyse survey, program and FarmerChat usage data using appropriate statistical methods.
- Coordinate field research, enumerators, sampling and data-quality processes.
- Convert research findings into recommendations for products, programs and strategic decisions.
- Maintain rigorous research documentation, datasets, codebooks and reproducible analytical workflows.
- Test emerging approaches such as voice-based research and AI-enabled MEL.
- Protect participant privacy and ensure research is conducted ethically.
Who This Job Is For
This role is well suited to an applied researcher or MEL professional with strong analytical foundations. Candidates should be comfortable working with quantitative data while also understanding qualitative research methods.
The strongest profiles will combine statistical ability with practical research judgment. Experience working across program, product and technology teams would be useful. Candidates interested in agriculture, digital advisory services and AI-enabled research may find the role particularly relevant.
The position is less suitable for candidates without hands-on research experience or without the ability to analyse datasets independently.
Requirements
Essential
- Bachelor's degree in economics, statistics, public policy, development studies, project management, agricultural economics, social sciences or another relevant field.
- Approximately 3–5 years of relevant MEL, applied research or evaluation experience; or a relevant Master's degree with approximately 1–3 years of experience.
- Experience designing quantitative surveys and research instruments.
- Strong Excel skills and working proficiency in Stata and/or R or related software.
- Understanding of applied statistics, including significance testing and hypothesis testing.
- Knowledge of sampling methods and sample-size calculations.
- Experience cleaning, managing and analysing research or program datasets.
- Experience supporting qualitative research.
- Strong data-quality and research-documentation skills.
- Ability to communicate research findings clearly to non-research audiences.
- Ability to collaborate across program, product and data or technology teams.
- Strong written and spoken English plus a local language.
- Curiosity and willingness to learn independently.
- Ability to travel for fieldwork.
Preferred
- Agriculture or smallholder-farmer experience in Kenya or East Africa.
- Experience with digital products, AI-enabled tools or technology-for-development programs.
- Experience combining survey data with digital usage or transactional data.
- Experience using SurveyCTO, KoboToolbox, ODK or similar platforms.
- Experience with randomized evaluations, A/B testing or quasi-experimental research.
- Experience with voice-based research or conversational surveys.
- Experience managing enumerators or external research firms.
- Knowledge of Kenya's agricultural sector and smallholder farming context.
Compensation and Benefits
Salary: Not disclosed in the vacancy.
The source does not specify bonuses, health benefits, retirement benefits, paid leave, relocation support or visa sponsorship. Candidates should verify these details with Digital Green before applying.
Location and Work Arrangement
Location: Nairobi, Kenya
Work arrangement: Not specified
Field travel: Required
The vacancy indicates that the role involves field travel. However, it does not specify a fixed number of travel days per month.
Why This Opportunity May Be Worth Considering
This role offers an unusual combination of MEL, applied research, agriculture, digital products and AI. It also provides opportunities to experiment with newer approaches to data collection and evaluation.
The position could suit researchers who want to move beyond conventional surveys and contribute directly to product and program decisions. It also offers room to take greater ownership of research and evaluation activities.
What to Check Before Applying
Review the qualification and experience requirements carefully before applying. Your application should demonstrate both research capability and practical data-analysis experience.
Candidates should also assess their willingness to travel for field research. Experience with digital platforms, agricultural programs or emerging research methods can strengthen an application, although these are listed as preferred rather than required qualifications.
How to Apply
The supplied vacancy text does not include a specific application URL, email address or application deadline.
Candidates should therefore use Digital Green's official recruitment channel and confirm the current application instructions before submitting their materials.
About the Employer
Digital Green is a global non-profit focused on helping smallholder farmers develop more prosperous and resilient livelihoods. Its work combines agricultural extension experience with digital technology and AI.
Its FarmerChat platform provides multilingual agricultural advice covering areas such as climate-resilient practices, pest management, inputs and market access. The platform operates across India, Kenya, Ethiopia, Nigeria and Brazil and supports multiple local languages.
AscendurePro Application Notes
Before you apply:
- Make your MEL or applied research experience immediately visible.
- Highlight survey design and research-instrument development.
- Show evidence of quantitative data analysis.
- List your proficiency in Excel, Stata and/or R clearly.
- Demonstrate experience with qualitative research where applicable.
- Highlight sampling, data-quality and evaluation experience.
- Include examples where research findings influenced decisions.
- Emphasize experience working across program, product or technology teams.
- Mention relevant agriculture or digital-development experience.
- Prepare examples showing curiosity, independent learning and methodological judgment.
CV focus: Prioritize measurable research outcomes, analytical methods, datasets handled, study designs, fieldwork responsibilities and recommendations that influenced programs or products.
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LIKELY QUESTIONS - How would you design a study to assess whether FarmerChat changes smallholder farmers' agricultural decisions and outcomes in Kenya or Nigeria? - Describe a survey or evaluation you have designed end to end, including the research question, sampling approach, instrument design, fieldwork, analysis and recommendations. - How would you combine FarmerChat product-usage data with survey data to understand user engagement, satisfaction and potential behavior change? - What statistical methods would you use to test whether farmers who use FarmerChat more frequently have different outcomes from less frequent or non-users, and what biases would you consider? - How do you determine an appropriate sample size for a farmer survey or impact evaluation? - Tell us about your experience cleaning and analysing complex datasets in R, Stata or another statistical package. What checks do you use to ensure data quality? - How would you coordinate enumerators across multiple counties or states while maintaining consistent consent, interview quality and data-security standards? - What opportunities and risks do you see in using AI-enabled or voice-based research methods with smallholder farmers? BEHAVIOURAL QUESTIONS - Tell me about a time you translated a complex research finding into a practical recommendation for a non-technical stakeholder. Model approach: Situation: Briefly describe the project, stakeholder and decision at stake. Task: Explain your responsibility for analysing and communicating the evidence. Action: Describe how you simplified the analysis, used clear visuals or farmer stories, explained limitations and tailored recommendations to the audience. Result: Quantify the decision, product or program change made and its subsequent effect. - Describe a time when you discovered a data-quality issue during research or fieldwork. Model approach: Situation: State the study context and the issue, such as missing data, inconsistent responses, enumerator error or duplicate records. Task: Explain your responsibility for protecting data integrity without unnecessarily delaying the study. Action: Describe your diagnostic checks, root-cause investigation, correction plan, enumerator retraining and documentation. Result: Share the improvement in data quality, records recovered or prevention measures introduced. - Tell me about a time you worked across teams with different priorities, such as program, product and technology colleagues. Model approach: Situation: Describe the shared initiative and the conflicting needs or assumptions. Task: Explain your role in aligning stakeholders around a useful research question and feasible approach. Action: Show how you set expectations, agreed definitions and metrics, created a decision timeline and communicated progress. Result: State the research output, decision enabled and improvement in cross-team collaboration. - Describe a time when a research result contradicted expectations or was not statistically significant. Model approach: Situation: Explain the original hypothesis and why stakeholders expected a positive result. Task: State your responsibility to deliver an accurate, useful interpretation. Action: Describe how you checked data quality, assessed power and assumptions, explored heterogeneity carefully and communicated uncertainty without overstating conclusions. Result: Explain the revised decision, follow-up study or product adjustment that resulted. SMART QUESTIONS TO ASK - What are the highest-priority decisions that the MEL and Research team needs to inform about FarmerChat over the next 12 months? - How is FarmerChat usage data currently structured and accessed, and what are the main challenges in linking it to survey, program or outcome data? - What evaluation designs has Digital Green used for FarmerChat to date, and where do you see the biggest remaining evidence gaps? - How do product, program and MEL teams currently turn research findings into changes in the FarmerChat experience or field implementation? - What would success look like for the person in this role after six months and after one year, including expected ownership of evaluations and field research? RED FLAGS TO WATCH FOR - Vague answers about research decision-making, especially if the company cannot identify specific product or program decisions that MEL findings have influenced. - Expectations for rigorous impact claims without access to reliable product data, adequate budgets, fieldwork timelines, comparison groups or sufficient sample sizes. - Unclear safeguards around farmer consent, data privacy, AI use, local-language research, enumerator management and data ownership across Kenya and Nigeria.
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Adjacent Career Paths
Roles you'd also qualify for based on this posting's requirements:
- Monitoring, Evaluation and Learning Specialist — The candidate has direct experience designing evaluations, analyzing program data, and managing research quality.
- User Researcher — The role's mixed-methods farmer research and product-data analysis align with user research for digital products.
- Impact Evaluation Analyst — The candidate is qualified to design studies, apply statistical methods, and assess program or product outcomes.
- Data and Research Analyst — Strong survey, administrative, and digital usage-data skills transfer directly to applied analytics roles.