(RFP301374) SENIOR ANALYST – DATA (INTERNAL ADVERT), IITA KENYA

SENIOR ANALYST – DATA (INTERNAL ADVERT)

THE ORGANIZATION

The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation. With novel partnerships, the Alliance generates evidence and mainstreams innovations to transform food systems and landscapes so that they sustain the planet, drive prosperity, and nourish people in a climate crisis. The Alliance is part of CGIAR, a global research partnership for a food-secure future. Please visit for more information on the Alliance.

The International Institute of Tropical Agriculture (IITA) is a not-for-profit institution that generates agricultural innovations to meet Africa’s most pressing challenges of hunger, malnutrition, poverty, and natural resource degradation. Working with various partners across sub-Saharan Africa, we improve livelihoods, enhance food and nutrition security, increase employment, and preserve natural resource integrity. IITA is a member of CGIAR, a global agriculture research partnership for a food secure future. Please visit for more information on IITA.

ABOUT THE POSITION

The Senior Analyst Data role is established to provide hands-on technical support for agronomic data management, analytics, and modelling to advance data-driven decision support tools and nutrient management research. The post holder will curate and standardize agronomic datasets, develop analytical and machine learning workflows, generate spatial and temporal crop insights, and support technical reporting, scientific publications, and stakeholder engagement. The role ensures that data, analytical methods, and modelling outputs are well managed, aligned with project objectives, and effectively translated into robust evidence for digital agronomy tools and research outcomes.

MAIN DUTIES AND RESPONSIBILITIES INCLUDE:

AGRONOMIC DATA MANAGEMENT

The Senior Analyst will support the implementation of agronomic data management, analytics, and modelling activities across multiple projects, countries, and partners. The role requires close collaboration with scientists, data managers, software developers, and external partners to deliver high-quality data products, machine learning models, and decision-support tools.

The role will involve:

• Curate, standardize, and manage agronomic and survey datasets using CAROB R-based workflows and FAIR data standards.
  
• Develop and implement analytical workflows in R for data processing, integration, augmentation, and analysis to support nutrient management research.
  
• Train, validate, and apply machine learning models to generate hyperlocal fertilizer recommendations and support digital agronomy tools.
  
• Develop and apply process-based and machine learning models for spatial and temporal crop yield prediction and agronomic decision support.
  
• Conduct statistical, geospatial, and integrated analyses of agronomic, soil, climate, and survey datasets to generate actionable insights.
  
• Support technical meetings, workshops, and consultations by communicating analytical methods and modelling approaches to multidisciplinary teams and partners.
  
• Prepare technical reports, research documentation, and scientific publications to disseminate project findings.
  
• Provide technical and analytical support to deliver project objectives and strengthen data-driven agronomic research.
  
• Maintain comprehensive documentation of datasets, scripts, analytical workflows, specifications, and data access procedures to ensure reproducibility and knowledge sharing.
  
  

AGRONOMIC DATA ANALYTICS AND MODELLING

The Senior Analyst will support agronomic data analytics and modelling, collaborating with internal and external stakeholders to support the development of evidence-based decision-support tools and research outputs.

The role will:

• Collaborate with scientists, data managers, software developers, and project teams to deliver high-quality analytical and modelling outputs.
  
• Engage with national and international research partners to support data sharing, harmonization, and implementation of FAIR data standards.
  
• Provide technical guidance on data management, analytical workflows, machine learning, and crop modelling approaches.
  
• Support technical meetings, workshops, and consultations by presenting analytical methods, modelling results, and research findings.
  
• Contribute technical inputs to multidisciplinary research activities and digital agronomy initiatives.
  
• Liaise with collaborators to facilitate access to agronomic, soil, climate, and survey datasets required for project implementation.
  
• Support the integration of analytical outputs into digital decision-support tools and research products.
  
• Contribute to scientific publications, technical reports, and knowledge-sharing activities with project partners and stakeholders.
  

AGRONOMIC DATA ANALYTICS AND DECISION SUPPORT

The Senior Analyst will support the delivery of high-quality agronomic data management, analytics, modelling, and decision-support products that strengthen digital agronomy research and evidence-based nutrient management.

The role will:

• Deliver high-quality, FAIR-compliant agronomic datasets using standardized CAROB R-based workflows and data standards.
  
• Develop and maintain robust R analytical pipelines for processing, integrating, and analysing agronomic, soil, climate, and survey data. Produce validated machine learning and process-based modelling outputs to support hyperlocal fertilizer recommendations and crop yield prediction.
  
• Generate statistical, geospatial, and data-driven insights that inform site-specific nutrient management and digital decision-support tools.
  
• Contribute technical inputs to project meetings, workshops, and stakeholder engagements, communicating analytical methods and modelling results.
  
• Prepare technical reports, research documentation, and scientific publications that disseminate analytical findings and project outcomes.
  
• Provide timely technical and analytical support to ensure successful delivery of project objectives and milestones.
  
• Maintain comprehensive documentation of datasets, scripts, analytical workflows, and metadata to ensure reproducibility, transparency, and long-term usability of project outputs.
  

REQUIREMENTS:

EDUCATION

Bachelor's degree in Agronomy, Crop Science, Agricultural Engineering, Agricultural Data Science, Statistics, Computer Science, Geoinformatics, Environmental Science, or a related field..

EXPERIENCE

• At least 5 years of relevant experience in agronomic data analysis, agricultural research, or data management.
  
• Demonstrated experience using R (or similar statistical programming languages) for data processing, analysis, and visualization.
  
• Experience working with agronomic, soil, climate, survey, or spatial datasets.
  
• Experience applying statistical methods and, preferably, machine learning techniques to agricultural datasets.
  
• Familiarity with geospatial analysis and GIS tools (e.g., QGIS, ArcGIS, or R spatial packages).
  
• Experience supporting crop modelling or agricultural decision-support systems is an added advantage.
  
• Experience preparing technical reports, documentation, and scientific outputs.
  
• Experience working in multidisciplinary research teams and collaborating with national and international partners.
  
• Demonstrated ability to manage multiple tasks, maintain well-documented analytical workflows, and deliver high-quality outputs within agreed timelines.
  

TECHNICAL COMPETENCIES

• Proficiency in R for data management, statistical analysis, and visualization.
  
• Good understanding of agronomy, crop production systems, and nutrient management principles.
  
• Knowledge of statistical analysis, data quality assurance, and data management best practices.
  
• Familiarity with machine learning methods and their application to agricultural datasets.
  
• Experience with geospatial analysis and GIS tools (e.g., QGIS, ArcGIS, or R spatial packages).
  
• Basic understanding of crop simulation models (e.g., APSIM, DSSAT) is an added advantage. Strong analytical, problem-solving, and quantitative reasoning skills.
  
• Ability to manage multiple tasks, prioritize work, and meet deadlines.
  
• Excellent documentation, technical writing, and reporting skills.
  
• Strong communication and interpersonal skills, with the ability to work effectively in multidisciplinary and multicultural teams.
  
• High level of attention to detail and commitment to producing high-quality, reproducible analytical outputs.
  
• Ability to work independently while contributing effectively within a collaborative research environment.
  
• Proficiency in written and spoken English is required.
  
• Knowledge of version control tools (e.g., Git/GitHub) and FAIR data principles is an added advantage.
  

EXPECTED DELIVERABLES

• FAIR-compliant agronomic, survey, soil, and climate datasets curated, standardized, and documented using CAROB R-based workflows.
  
• Reproducible R scripts and analytical workflows for data processing, quality control, integration, and statistical analysis.
  
• Validated machine learning models and hyperlocal fertilizer recommendation products supporting digital agronomy and decision-support tools.
  
• Spatial and temporal crop yield prediction outputs generated using process-based and machine learning models.
  
• Statistical, geospatial, and integrated analyses completed to support site-specific nutrient management and agronomic research.
  
• Technical documentation, metadata, and workflow specifications maintained to ensure reproducibility and adherence to FAIR principles.
  
• Technical reports, analytical summaries, and presentations produced to communicate modelling results and research findings.
  
• Scientific manuscripts, conference abstracts, and other research outputs prepared in collaboration with project teams.
  
• Technical contributions provided to project meetings, workshops, stakeholder consultations, and digital agronomy initiatives.
  
• High-quality analytical outputs delivered on time, supporting project milestones, decision-support tool development, and evidence-based agronomic research.
  

KEY PERFORMANCE MILESTONES

• FAIR-compliant agronomic and survey datasets curated, standardized, and documented using CAROB R-based workflows.
  
• Reproducible R analytical pipelines for data processing, quality control, integration, and statistical analysis.
  
• Validated machine learning models and hyperlocal fertilizer recommendation outputs for digital decision-support tools.
  
• 
  
• Spatial and temporal crop yield prediction outputs generated using process-based and machine learning models.
  
• Statistical, geospatial, and integrated analyses of agronomic, soil, climate, and survey datasets to support nutrient management research.
  
• Technical reports, analytical summaries, and documentation that communicate project findings and modelling results.
  
• Scientific manuscripts, conference presentations, and other research outputs contributing to project dissemination.
  
• Well-documented scripts, workflows, metadata, and technical specifications to ensure reproducibility and compliance with FAIR principles.
  
• Technical inputs provided to project meetings, workshops, and stakeholder consultations to support evidence-based decision making.
  
• Timely delivery of high-quality analytical outputs that contribute to project milestones, digital agronomy tools, and research objectives.
  
  

EVALUATION CRITERIA

• Relevance and quality of academic qualifications in agronomy, agricultural data science, statistics, computer science, or a related field.
  
• Demonstrated experience in agronomic data management, statistical analysis, and data curation.
  
• Proficiency in R (or equivalent statistical programming languages) for data processing, analysis, and visualization.
  
• Experience applying machine learning and/or crop modelling approaches to agricultural research.
  
• Experience working with agronomic, soil, climate, survey, and geospatial datasets.
  
• Demonstrated ability to develop reproducible analytical workflows and maintain high-quality technical documentation.
  
• Quality of technical writing, reporting, and contribution to scientific publications or research outputs.
  
• Ability to interpret analytical results and communicate technical findings to multidisciplinary teams.
  
• Evidence of effective collaboration in multidisciplinary and multicultural research environments.
  
• Strong analytical, problem-solving, organizational, and time management skills.
  
• Proficiency in written and spoken English. Knowledge of French is an added advantage.
  

TERMS OF EMPLOYMENT

This is a nationally recruited position based at IITA Nairobi, Kenya. The initial contract will be for one and half years subject to a probation period of three months and is renewable depending on performance and availability of resources.

This position is graded at BG07 level, with a minimum basic salary of KES 227,931.00 in a scale of BG01 to BG14 (BG14 being the highest level according to the Alliance job classification framework policy). We offer a competitive salary and excellent benefits including but not limited to insurance, retirement plan, staff training and development, paid time off and flexible working arrangements.

The Alliance Bioversity-CIAT is committed to fair, safe, and inclusive workplaces. We believe that diversity powers our innovation, contributes to our excellence, and is critical for our mission. Recruiting and mentoring staff to create an inclusive organization that reflects our global character is a priority. We encourage applicants from all cultures, races, colors, religions, sexes, national or regional origins, ages, disability statuses, sexual orientations, marital status, and gender identities. Female candidates are strongly encouraged to apply.

APPLICATIONS

Applicants are invited to visit to get full details of the position and to submit their applications. Applications MUST include reference number Ref: (RFP301374 )- SENIOR ANALYST- DATA the position applied for. Application and CV should be saved as one document using the candidate’s lastname, firstname for ease of sorting.

Note: The Alliance does not charge a fee at any stage of the recruitment process (application, interview meeting, processing or training). The Alliance also does not concern itself with information on applicants' bank accounts.

Applications closing date:27th July 2026

Please note that email applications will not be considered. Only short-listed candidates will be contacted.