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.

In Zambia, from our hub in Kabwe, we run a specialised climate-smart input and agroforestry program to address challenges such as irregular rainfall and soil degradation. By partnering with local Tree Nursery Operators, we raise and distribute agroforestry trees that enrich soils and build long-term financial resilience. Supported by a structured frontline field team across Central and Southern Provinces, we use rigorous, data-driven frameworks to measure our impact and ensure we drive meaningful improvements in farmer yields, asset accumulation, and food security.

About the Role

We are seeking an exceptional MEL professional with 4+ years of experience to run our day-to-day field data collection operations, and statistical analysis, strengthen data quality, and manage our 13-person field implementation teams. You will coordinate the entire survey lifecycle from questionnaire scripting and data cleaning to field deployment across our operational districts, ensuring our climate-smart agriculture and agroforestry programs are backed by bulletproof evidence to drive impact for smallholder farmers in Zambia. As the team’s direct link to our Global MEL team, you’ll be based in Kabwe and report to our Research and Data Lead.

Responsibilities

Survey Design & Data Management

• Design statistically sound data collection instruments and survey methodologies tailored to rural agricultural interventions.
• Establish rigorous data management workflows, cleaning protocols, and automated quality assurance checks to minimise data errors.
• Continuously audit and improve data collection processes to streamline reporting pipelines.

Survey Analysis

• Analyse survey data using statistical software (R, Python, etc.) to produce summary statistics and rigorous impact analysis and other statistical analysis as needed (e.g. regression, multi-level modeling, and difference-in-difference methods).
• Apply appropriate statistical approaches, including parametric and non-parametric methods, based on the nature of the data.
• Translate findings into clear, actionable recommendations for program and leadership teams, including contributing to the yearly impact report.

Field Implementation & Team Management

• Coordinate and execute large-scale field survey implementations from inception to final dataset delivery across Central and Southern Provinces.
• Directly manage, mentor, and build the capacity of the field implementation team and supervisor networks.
• Plan complex field logistics, timelines, and deployment strategies for remote, rural data collection exercises.
• Guarantee field data integrity by enforcing strict validation protocols and back-checking procedures.

Cross-Functional Collaboration & Execution

• Work closely with operational managers and global technical advisors, actively integrating feedback to iterate on survey designs
• Proactively identify and resolve operational and logistical bottlenecks in the field
• Take absolute ownership of final dataset accuracy, operational metrics, and team deliverables.
• Serve as a key point of contact with the Global MEL team, sharing performance data and program learnings.

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:

• Knowledge of R, Python, or suitable languages, combined with survey tools (e.g., CommCare, ODK, KoboToolbox), and Google Sheets.
• Proficiency in quantitative/qualitative survey design, handling complex datasets, with a solid grounding in statistics
• Prior experience directly supervising field teams, enumerator networks, or data collectors in rural environments.
• Demonstrated capability in training, coaching, and elevating the technical execution and data capacity of junior field staff.
• Ability to look ahead, manage team timelines, and resolve personnel or logistical field issues before they impact data quality.
• Bachelor’s degree in Economics, Statistics, Agricultural Economics, Development Studies, or a related field. Masters degree (or higher) preferred.

Preferred Start Date

As soon as possible

Job Location

Kabwe, Zambia

Benefits

Health insurance, paid time off

Contract Duration

1 year

Eligibility

This role is only open to citizens or permanent residents of Zambia.

• Design statistically sound data collection instruments and survey methodologies tailored to rural agricultural interventions.
• Establish rigorous data management workflows, cleaning protocols, and automated quality assurance checks to minimise data errors.
• Continuously audit and improve data collection processes to streamline reporting pipelines.
• Analyse survey data using statistical software (R, Python, etc.) to produce summary statistics and rigorous impact analysis and other statistical analysis as needed (e.g. regression, multi-level modeling, and difference-in-difference methods).
• Apply appropriate statistical approaches, including parametric and non-parametric methods, based on the nature of the data.
• Translate findings into clear, actionable recommendations for program and leadership teams, including contributing to the yearly impact report.
• Coordinate and execute large-scale field survey implementations from inception to final dataset delivery across Central and Southern Provinces.
• Directly manage, mentor, and build the capacity of the field implementation team and supervisor networks.
• Plan complex field logistics, timelines, and deployment strategies for remote, rural data collection exercises.
• Guarantee field data integrity by enforcing strict validation protocols and back-checking procedures.
• Work closely with operational managers and global technical advisors, actively integrating feedback to iterate on survey designs
• Proactively identify and resolve operational and logistical bottlenecks in the field
• Take absolute ownership of final dataset accuracy, operational metrics, and team deliverables.
• Serve as a key point of contact with the Global MEL team, sharing performance data and program learnings.

• R, Python, CommCare, ODK, KoboToolbox, Google Sheets
• Quantitative/qualitative survey design
• Handling complex datasets
• Statistics
• Field team supervision
• Training and coaching
• Logistical planning
• Problem-solving

• Bachelor’s degree in Economics, Statistics, Agricultural Economics, Development Studies, or a related field. Masters degree (or higher) preferred.
• 4+ years of experience
• Knowledge of R, Python, or suitable languages, combined with survey tools (e.g., CommCare, ODK, KoboToolbox), and Google Sheets.
• Proficiency in quantitative/qualitative survey design, handling complex datasets, with a solid grounding in statistics
• Prior experience directly supervising field teams, enumerator networks, or data collectors in rural environments.
• Demonstrated capability in training, coaching, and elevating the technical execution and data capacity of junior field staff.
• Ability to look ahead, manage team timelines, and resolve personnel or logistical field issues before they impact data quality.