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 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.

Application Deadline

10 July, 2026

Please note that we hire on a rolling basis, which means that applications are reviewed and processed on a continuous basis until a hire is made.

One Acre Fund never asks candidates to pay any money or pay for tests at any stage of the interview process. Official One Acre Fund emails will always arrive from an @oneacrefund.org address. Please report any suspicious communication here (globalhotline@oneacrefund.org), but do not send applications or application materials to this email address.

Diversity, Equity, Inclusion (DEI), and anti-racism are deeply connected to our organisation’s mission and purpose. One Acre Fund aspires to build a culture where all staff feel consistently valued, represented, and connected – so that our team can thrive as professionals, and achieve exceptional impact for the farmers we serve.

We are committed to equal employment opportunity regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression. We are proud to be an equal opportunity workplace.

About the role:

We are looking for highly skilled AI Evaluators to support the development of advanced AI tools for grammar correction and proofreading. This project focuses on creating a rich and diverse dataset of written German text, annotated and corrected by humans.

Responsibilities:

• Respond to a set of prompts simulating realistic business or casual communication.
• Write clear, plausible short texts (3–4 sentences for business settings, 1–2 sentences for casual chat).
• Generate responses with intentional but realistic grammar, spelling, and structural errors.
• Review annotated message pairs (input text + corrected/proofread version) to ensure:
• All grammar/spelling/preposition/punctuation errors are corrected appropriately
• Meaning is preserved between input and corrected text
• Error types are classified correctly
• Explanations are clear and linguistically sound

Qualifications:

• German as a first language - C1 German proficiency 
• English as a second language - B2 English proficiency.
• Ability to follow linguistic and situational writing prompts.
• Good written communication and creativity.
• 2 years working experience in a corporate/ business environment.
• 1 year working experience working as an AI Evaluator, data annotator, or similar role 
• Strong computer literacy and fast, accurate typing skills are essential for navigating annotation tools and maintaining efficiency in text-based tasks.
• Strong logic, intuitive problem-solving and critical thinking; adaptability to the needs of the task. The candidate needs to be open-minded and welcome different viewpoints or solutions.
• Proactiveness, confidence to work independently without constant support. Reliability and being able to make good decisions on their own.
• Be a thought partner to delivery managers, input into the project delivery strategies, and share quality best practices to aid in achieving the project goals.
• Adapt quickly to evolving guidelines, new task types, and AI model updates in a fast-paced, iterative environment. Access to your own laptop for work 

Preferred Qualifications:

• C1 or C2 English preferred 
• Experience working with NLP/GenAI models is a plus 

NB: This is a temporary, project-based position with the possibility of renewal. While this role will be on payroll, please note that benefits are not included as part of this position.

About the role:

We are looking for a highly skilled Quality Analyst Project Coordinator to support the development of advanced AI tools for product attribution, classification and other purposes. This project focuses on creating a rich and diverse dataset of written German text, annotated and corrected by humans.

Responsibilities:

• Review and audit labeled datasets for both text and image-based content to ensure accuracy, consistency, and adherence to guidelines.
• Document and share feedback with the individual labelers
• Identify trends and insights in quality performance, and effectively mitigate quality performance issues. 
• Communicate quality trends and insights to the customer. Calibrate with the  customer on instructions and edge cases.
• Evaluate responses generated by AI, verifying correctness, clarity, relevance, and alignment with task instructions.
• Apply knowledge of Consumer Packaged Goods domain to accurately classify and/or describe products.
• Collaborate with cross-functional teams to update labeling workflows and improve quality.
• Monitor performance metrics (e.g., accuracy rates, inter-rater agreement) and contribute to continuous improvement initiatives.
• Generate responses with intentional but realistic grammar, spelling, and structural errors.
• calibration sessions to align QA standards across teams and help train new reviewers or analysts.
• Adapt quickly to evolving guidelines, new task types, and AI model updates in a fast-paced, iterative environment.
• Ensure that documentation is up to date. 

Qualifications:

• English as a second language - minimum C1 English proficiency required. 
• C2 level of German
• Ability to follow linguistic and situational writing prompts.
• Excellent written communication and coordination skills 
• 2 years working experience in a corporate/ business environment.
• Prior experience in at least one of the following:
• Linguistic QA
• Data annotation for NLP/AI projects
• ESL/EFL teaching, editing, or copyediting
• Keen eye for detail -spotting subtle grammar and structure issues others miss.
• Comfortable reviewing and providing feedback on structured data.
• Ability to give actionable and constructive feedback to annotators.
• Experience with creating and tracking timelines, milestones, and status reports is a plus.

Preferred Qualifications:

• C1 or C2 level English proficiency. 
• Familiarity with error tagging systems is a big plus.
• Experience in CPG industry/workflows, or other relevant knowledge of retail products. 

NB: This is a temporary, project-based position with the possibility of renewal. While this role will be on payroll, please note that benefits are not included as part of this position.