DG
Digital Green
MEL & Research Manager at Digital Green
KenyaFull TimeMid-Senior Level
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Digital Green
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About the role
Digital Green is seeking a MEL & Research Manager based in Nairobi to support monitoring, evaluation, research, and learning for our work in Kenya and Nigeria, with a particular focus on FarmerChat, Digital Green's AI-powered agricultural advisory platform.
This is a hands-on research and analytics role at the intersection of agriculture, digital technology and AI. The MEL Research Manager will help us understand who is using FarmerChat, how farmers engage with the platform, whether the information and advisory they receive is useful and trusted, how farmers act on that information, and ultimately whether FarmerChat contributes to meaningful improvements in farmers' practices, productivity, income, as well as agency and resilience.
The role will combine traditional MEL and research methods with analysis of digital product data and experimentation with new approaches to measurement and learning. This may include designing and analyzing farmer surveys, conducting qualitative research, developing sampling strategies, and linking self-reported outcomes with FarmerChat backend data to better understand patterns of adoption, engagement, and impact.
At the same time, we want to test new ways of collecting data and learning from farmers—including voice-based methods, AI-enabled or agentic MEL approaches, alternative survey formats, and different approaches to incentivizing participation. The MEL Research Manager should be curious and willing to experiment, while also thinking critically about the strengths, limitations, biases, data quality, cost, farmer experience, and ethical implications of different methods. We are looking for someone who can help us test what works, learn from what does not, and identify when a new approach offers real advantages over more traditional methods.
The Manager will report to the Director of MEL and work closely with Digital Green's Kenya program, product, user insights/ux research, and technology teams, as well as external research and implementation partners.
We are looking for someone who has already developed a strong foundation in MEL or applied research but is eager to continue developing their technical and leadership skills. This is an opportunity for a curious and ambitious researcher to take increasing ownership of research and evaluation while contributing to Digital Green's broader evidence agenda for FarmerChat.
What you will do
Design research and measurement for FarmerChat
Support the design and implementation of MEL studies and evaluations that assess FarmerChat's reach, engagement, user experience, behavior change, and outcomes for farmers.
Translate program and product questions into clear research questions, hypotheses, indicators, and measurement approaches.
Design high-quality quantitative survey instruments covering areas such agricultural practices, relevance and trust of advisory, adoption of recommendations, productivity, costs, income, agency, resilience, and other farmer outcomes
Design qualitative interview and focus group guides to explore farmers' experiences with FarmerChat, including how and why they use it, what they find useful, barriers to engagement, and how advisory influences their decisions.
Contribute to research design decisions, including sampling approaches, sample-size calculations, statistical power, comparison groups, and timing of measurement.
Support experimental and quasi-experimental studies, including randomized evaluations and A/B tests, where appropriate.
Help design and test alternative approaches to data collection, including voice-based research, AI-enabled or agentic MEL, shorter or adaptive survey formats, and different approaches to participant incentives.
Assess the trade-offs between different methods, including validity, representativeness, bias, cost, response rates, data quality, and participant experience.
Analyze farmer and product data
Clean, manage, and analyze quantitative data using Excel and Stata and/or R.
Analyze FarmerChat backend and product-usage data alongside survey and qualitative data to build a richer understanding of farmer engagement and outcomes.
Examine patterns such as frequency and intensity of FarmerChat use, types of questions farmers ask, agricultural value chains of interest, retention and drop-off, and differences across farmer segments, including gender based analysis
Conduct descriptive and inferential statistical analysis, including significance testing, confidence intervals, hypothesis testing, and subgroup analysis.
Link survey responses with FarmerChat usage data where appropriate to explore relationships between digital engagement, adoption of advisory, and farmer outcomes.
Work closely with product and data colleagues to define meaningful metrics and ensure that data generated through FarmerChat can support MEL and research questions.
Compare findings across different data-collection approaches and help determine which methods are most appropriate for different research questions.
Lead high-quality field research
Coordinate quantitative and qualitative data collection with farmers in Kenya and Nigeria, including working with field teams, enumerators, and external research firms.
Develop sampling plans and support recruitment of farmers for research.
Train and support enumerators and research teams on survey instruments and research protocols.
Monitor incoming data and conduct systematic data-quality checks throughout data collection.
Conduct or support farmer interviews, focus groups, and other qualitative research.
Pilot new data-collection approaches and document lessons on what works, for whom, and under what conditions.
Ensure research is conducted ethically and that farmers' privacy and data are appropriately protected.
Turn evidence into learning and action
Synthesize quantitative, qualitative, and FarmerChat usage data to answer practical questions for Digital Green.
Produce clear research briefs, presentations, reports, and other materials for internal teams, partners, and donors.
Translate findings into practical recommendations for FarmerChat product development and program implementation.
Work with program teams to understand whether and how FarmerChat is contributing to changes in agricultural knowledge, practices, productivity, costs, income, and other outcomes.
Contribute evidence and analysis for donor reporting, proposals, learning agendas, and strategic decision-making.
Document not only successful approaches but also experiments that do not work, so that learning can inform future research and product decisions.
Build strong research systems and documentation
Maintain rigorous documentation of research designs, survey instruments, sampling decisions, datasets, codebooks, analysis files, statistical code, assumptions, and methodological decisions.
Develop reproducible and well-organized analytical workflows, so that analyses can be reviewed, updated, and built upon over time.
Document pilots of new MEL approaches, including methodology, implementation challenges, data-quality issues, results, and recommendations.
Help strengthen Digital Green's MEL systems and approaches for measuring the performance and impact of digital and AI-enabled agricultural advisory.
Ensure that lessons from individual studies are captured and used to improve future research and measurement.
What we are looking for
Required qualifications
Bachelor's degree in economics, statistics, public policy, development studies, project management, agricultural economics, social sciences, or another relevant quantitative or research-oriented field, with approximately 3–5 years of relevant experience in MEL, applied research, or evaluation; or a relevant Master's degree with approximately 1–3 years of relevant experience.
Demonstrated experience designing quantitative surveys and other research instruments.
Strong analytical skills, including proficiency in Excel and working proficiency in Stata and/or R or related software
Solid understanding of applied statistics, including descriptive statistics, confidence intervals, statistical significance, and hypothesis testing.
Understanding of sampling methods and sample-size calculations; familiarity with statistical power and power calculations.
Experience cleaning, managing, and analyzing survey or program datasets.
Experience conducting or supporting qualitative research, including interviews and/or focus groups.
Strong attention to data quality and experience developing or implementing data-quality checks.
Excellent documentation skills, including the ability to clearly document research methods, instruments, datasets, analytical decisions, and findings.
Ability to translate data and research findings into clear insights and recommendations for colleagues who are not researchers.
Ability to work effectively across teams, particularly with program, product, and data/technology colleagues.
Strong written and spoken English + local language
Demonstrated curiosity and willingness to learn new tools, analytical approaches, and research methods independently.
Ability to travel (X days per month) in the field.
Nice to have
Experience working in agriculture or with smallholder farmers in Kenya or East Africa.
Experience conducting MEL or research for digital products, digital advisory services, AI-enabled tools, or other technology-for-development programs.
Experience analyzing product or administrative data in addition to traditional survey data.
Experience linking survey data with digital usage or transactional datasets.
Experience with SurveyCTO, KoboToolbox, ODK, or similar digital data-collection platforms.
Experience with randomized evaluations, A/B testing, quasi-experimental methods, and impact evaluation.
Experience with voice-based research, conversational surveys, AI-enabled research tools, or other emerging approaches to data collection.
Experience testing participant incentives or other approaches to improving participation and response rates.
Experience managing external research firms, enumerators, or field research teams.
Knowledge of Kenya's agricultural sector and smallholder farming context.