Global Data R&D Analyst

About One Acre Fund

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 int