Senior Analyst - Data

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

http://www.alliancebioversityciat.org/

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

http://www.iita.org/

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..
• At least 5
• 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 yearssubject
• 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.
• At Impactpool we do our best to provide you the most accurate info, but closing dates may be wrong on our site. Please check on the recruiting organization's page for the exact info. Candidates are responsible for complying with deadlines and are encouraged to submit applications well ahead.
• Before applying, please make sure that you have read the requirements for the position and that you qualify. Applications from non-qualifying applicants will most likely be discarded by the recruiting manager.
• Summary by Impactpool
• The Senior Analyst – Data position at IITA Kenya focuses on providing technical support for agronomic data management, analytics, and modeling to enhance data-driven decision-making in nutrient management research. The role involves curating and standardizing datasets, developing analytical workflows, and generating insights to support digital agronomy tools. Collaboration with scientists and stakeholders is essential to deliver high-quality data products and research outputs. The position requires strong analytical skills and proficiency in R, along with experience in agronomic data analysis and machine learning techniques.
• Candidate Requirements:
• Bachelor's degree in relevant field
• At least 5 years of experience in agronomic data analysis
• Proficiency in R for data management and analysis
• Experience with agronomic, soil, climate, and survey datasets
• Familiarity with machine learning and geospatial analysis
• Ability to prepare technical reports and scientific outputs
• Strong communication and interpersonal skills
• Experience in multidisciplinary research teams
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• UNIRMCT - United Nations International Residual Mechanism for Criminal Tribunals
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• BG07 level
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