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