Founded in 2006, One Acre Fund equips 5.5 million smallholder farmers to make their farms more productive. Across nine countries that together are home to two-thirds of Africa's farmers, we provide high-quality farm supplies, tree seedlings, accessible credit, modern agronomic training, and a wide range of other agricultural services. On average, this model enables any farmer to increase their income and assets on supported land by more than 35 percent, while permanently improving their resilience. This is all made possible by our team of 9,000+ full-time staff, drawn from diverse backgrounds and professions. To learn more, please see our Why Work Here blog post.

About the Role

From R&D to sales to strategy to operations, the Global R&D Data Analyst has the unique opportunity to improve decision-making across all aspects of One Acre Fund’s program using many diverse data types, such as sales, yield, demographic and satellite data, to help us reach more farmers with greater impact.

The Global R&D Data Analyst will help us reach over one million farmers by executing analyses for strategic decision-making on repayment, expansion, and other business functions, and work directly with program leaders to interpret results and make data-driven decisions. The Global R&D Data Analyst will play an integral role in shaping One Acre Fund’s data strategy, including dreaming up and executing new ways to use our data to improve our program.

Additionally, One Acre Fund has a robust agronomic and socioeconomic research program spanning all countries of operation. This role will work closely with country R&D teams to ensure all trials are executed at the highest possible standards, provide follow-up analytical support and training to team members, and support with warehousing of our agronomic data to make our research outputs accessible to external collaborators, further increasing One Acre Fund’s smallholder farmer impact across the continent.

To succeed in this role, you will need to be a strong communicator and have a solid analytical background with experience in experimental design. You will need to be comfortable interpreting ambiguous results generated with imperfect data and advising leaders on the relative risk associated with different decisions based on the results of your analysis.

This is a deliberately hybrid role. Success requires the ability to operate effectively as:

• an experimental methodologist (trial design & causal inference),
• an applied data scientist (production analytics, geospatial methods, modelling), and
• a delivery-oriented project manager (prioritisation, documentation, coordination).

Responsibilities

Own methodological rigour and analytical quality for trials and surveys (30%):

• Design and analyse trials and surveys, including:
  • Sample size and power calculations
  • Stratification and experimental design
  • Recommend the appropriate statistical methods (e.g., hypothesis testing, regression, ANOVA/mixed models)
  • Lead analysis of agronomic and product trials to estimate treatment effects and program impact
  • Quality assure trial designs and analyses produced by other analysts
  • Translate trial findings into clear recommendations for product design, agronomic guidance, and program strategy.

Develop scalable analytical products and decision-support tools using program, survey, and spatial data (30%):

• Build, maintain, and improve analytical pipelines and production codebases that power operational decision tools (e.g., sowing date or input recommendations), including occasional support at the production level.
• Integrate survey, MEL, and operational data with geospatial layers (soil, climate, vegetation, remote sensing) to generate localised recommendations and program targeting strategies.
• Conduct spatial and remote-sensing analyses for program design, prioritisation, and impact estimation (e.g., soil erosion modelling, site suitability analysis).
• Analyse historical trial and soil data to generate input and soil management recommendations (e.g., lime application, fertiliser rate application).
• Evaluate potential impact of alternative interventions and support pilot design, iteration, and scale decisions.
• Translate analyses into decision-ready outputs (briefs, dashboards, and memos) for non-technical stakeholders.
• Identify new, high-leverage analytical use cases that improve program reach, impact, or cost-effectiveness.

Lead impact data management and project management (~20%)

• Lead curation and standardisation of historical yield, agronomic practice, and trial datasets to enable reuse and external research collaboration.
• Own knowledge management for impact data and trials, including:
  • Central documentation of methodologies, assumptions, sample sizes, and results for all projects
  • Reusable analysis templates and reference implementations
  • Manage external data requests in compliance with client data protection and confidentiality protocols.

Provide portfolio-level project management (~20%)

• Maintain project plans, priorities, and timelines
• Track dependencies and risks
• Coordinate with program and R&D stakeholders to identify potential delivery risks
• Establish durable documentation and planning systems (e.g., project roadmaps, project trackers, shared repositories).

Career Growth and Development

We have a strong culture of constant learning, and we invest in developing our people. You’ll have weekly check-ins with your manager, access to mentorship and training programs, and regular feedback on your performance. We hold career reviews every six months and set aside time to discuss your aspirations and career goals. You’ll have the opportunity to shape a growing organization and build a rewarding long-term career.

Qualifications

Across all roles, these are the general qualifications we look for. For this role specifically, you will have:

• Bachelor's Degree in one of the following fields: economics, econometrics, mathematics, or statistics
• Proficiency in R and/or Python, including working knowledge of -
  • Database connectivity (e.g., PostgreSQL) to enable data retrieval, manipulation, and storage from various databases
  • Interact with RESTful APIs (e.g., JSON, XML)
  • Data manipulation libraries (e.g., dplyr, tidyr) for efficient data wrangling, transformation, and exploration
  • Packages for data visualisation (e.g., ggplot2, lattice, plotly)
  • Advanced statistical analysis and modelling (stats, lme4, survival)
  • Machine learning frameworks (e.g., randomForest, xgboost, caret) for building predictive models and conducting machine learning tasks
  • Packages for data manipulation and visualisation, such as numpy, pandas, and Matplotlib
  • Spatial data manipulation libraries (geopandas, rasterio, shapely, GDAL)
• In-depth knowledge of statistically rigorous trial design methodologies, including RCTs, side-by-side comparisons, RCBD, and other experimental designs
• In-depth knowledge of statistically rigorous survey design methods, including random, stratified, and cluster sampling
• Knowledge of relevant meteorological, soil, and vegetation data layers and sources, their uses and limitations (e.g., iSDA, ISRIC, CHIRPS, TAMSAT, NASA, Sentinel Hub, Google Earth Engine, AWS)
• Working knowledge of agronomy, e.g., practical understanding of fertiliser nutrient compositions, plant populations, growing degree days, GxE interactions.
• Working knowledge of GIS tools such as ArcGIS and QGIS, and spatial data analysis techniques.
• Proficiency in version control, such as Git/GitHub.
• Ability to easily explain technical concepts to non-technical decision makers and grow the analytical skills of others.
• Excellent written and verbal communication skills for coordinating across teams.
• Demonstrated ability to manage multiple analytical projects in parallel, including:
  • Scoping ambiguous analytical problems and translating them into deliverable plans
  • Prioritising work against timelines and stakeholder needs
  • Tracking progress, managing dependencies, and proactively unblocking delivery risks
  • Maintaining clear documentation and project plans to ensure continuity and institutional memory

Preferred Start Date

As soon as possible

Job Location

Nairobi, Kenya or Kigali, Rwanda

Benefits

Health insurance, housing, and comprehensive benefits

Contract Duration

2 Years

Eligibility

One Acre Fund can support a work permit for this role. However, nationals of (or those with an extensive professional background and work history in) our countries of operation are preferred.

• Design and analyse trials and surveys, including: Sample size and power calculations, Stratification and experimental design, Recommend the appropriate statistical methods (e.g., hypothesis testing, regression, ANOVA/mixed models), Lead analysis of agronomic and product trials to estimate treatment effects and program impact, Quality assure trial designs and analyses produced by other analysts, Translate trial findings into clear recommendations for product design, agronomic guidance, and program strategy.
• Build, maintain, and improve analytical pipelines and production codebases that power operational decision tools (e.g., sowing date or input recommendations), including occasional support at the production level.
• Integrate survey, MEL, and operational data with geospatial layers (soil, climate, vegetation, remote sensing) to generate localised recommendations and program targeting strategies.
• Conduct spatial and remote-sensing analyses for program design, prioritisation, and impact estimation (e.g., soil erosion modelling, site suitability analysis).
• Analyse historical trial and soil data to generate input and soil management recommendations (e.g., lime application, fertiliser rate application).
• Evaluate potential impact of alternative interventions and support pilot design, iteration, and scale decisions.
• Translate analyses into decision-ready outputs (briefs, dashboards, and memos) for non-technical stakeholders.
• Identify new, high-leverage analytical use cases that improve program reach, impact, or cost-effectiveness.
• Lead curation and standardisation of historical yield, agronomic practice, and trial datasets to enable reuse and external research collaboration.
• Own knowledge management for impact data and trials, including: Central documentation of methodologies, assumptions, sample sizes, and results for all projects, Reusable analysis templates and reference implementations, Manage external data requests in compliance with client data protection and confidentiality protocols.
• Maintain project plans, priorities, and timelines
• Track dependencies and risks
• Coordinate with program and R&D stakeholders to identify potential delivery risks
• Establish durable documentation and planning systems (e.g., project roadmaps, project trackers, shared repositories).

• Proficiency in R and/or Python
• Database connectivity (e.g., PostgreSQL)
• Interact with RESTful APIs (e.g., JSON, XML)
• Data manipulation libraries (e.g., dplyr, tidyr)
• Packages for data visualisation (e.g., ggplot2, lattice, plotly)
• Advanced statistical analysis and modelling (stats, lme4, survival)
• Machine learning frameworks (e.g., randomForest, xgboost, caret)
• Packages for data manipulation and visualisation (numpy, pandas, Matplotlib)
• Spatial data manipulation libraries (geopandas, rasterio, shapely, GDAL)
• Statistically rigorous trial design methodologies
• Statistically rigorous survey design methods
• Knowledge of meteorological, soil, and vegetation data layers and sources
• Working knowledge of agronomy
• Working knowledge of GIS tools (ArcGIS, QGIS) and spatial data analysis techniques
• Proficiency in version control (Git/GitHub)
• Ability to explain technical concepts to non-technical decision makers
• Excellent written and verbal communication skills
• Demonstrated ability to manage multiple analytical projects in parallel

• Bachelor's Degree in economics, econometrics, mathematics, or statistics
• In-depth knowledge of statistically rigorous trial design methodologies, including RCTs, side-by-side comparisons, RCBD, and other experimental designs
• In-depth knowledge of statistically rigorous survey design methods, including random, stratified, and cluster sampling
• Knowledge of relevant meteorological, soil, and vegetation data layers and sources, their uses and limitations (e.g., iSDA, ISRIC, CHIRPS, TAMSAT, NASA, Sentinel Hub, Google Earth Engine, AWS)
• Working knowledge of agronomy, e.g., practical understanding of fertiliser nutrient compositions, plant populations, growing degree days, GxE interactions.
• Working knowledge of GIS tools such as ArcGIS and QGIS, and spatial data analysis techniques.
• Proficiency in version control, such as Git/GitHub.
• Ability to easily explain technical concepts to non-technical decision makers and grow the analytical skills of others.
• Excellent written and verbal communication skills for coordinating across teams.
• Demonstrated ability to manage multiple analytical projects in parallel, including: Scoping ambiguous analytical problems and translating them into deliverable plans, Prioritising work against timelines and stakeholder needs, Tracking progress, managing dependencies, and proactively unblocking delivery risks, Maintaining clear documentation and project plans to ensure continuity and institutional memory