One Acre Fund is a nonprofit organization that supplies smallholder farmers in East Africa with asset-based financing and agriculture training services to reduce hunger and poverty.

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Global R&D Data Analyst (Fixed-Term)

Job Type

Contract

Qualification

BA/BSc/HND

Experience

Location

Nairobi

Job Field

Data, Business Analysis and AI , ICT / Computer

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

• 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).

• 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, such as numpy, pandas, and Matplotlib
• Spatial data manipulation libraries (geopandas, rasterio, shapely, GDAL)
• Statistically rigorous trial design methodologies
• Statistically rigorous survey design methods

• Bachelor's Degree in economics, econometrics, mathematics, or statistics
• Proficiency in R and/or Python
• Working knowledge of database connectivity (e.g., PostgreSQL)
• Working knowledge of interacting with RESTful APIs (e.g., JSON, XML)
• Working knowledge of data manipulation libraries (e.g., dplyr, tidyr)
• Working knowledge of packages for data visualisation (e.g., ggplot2, lattice, plotly)
• Working knowledge of advanced statistical analysis and modelling (stats, lme4, survival)
• Working knowledge of machine learning frameworks (e.g., randomForest, xgboost, caret)
• Working knowledge of packages for data manipulation and visualisation, such as numpy, pandas, and Matplotlib
• Working knowledge of 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