Agroecological Systems Analyst at World Agroforestry Centre

1 month ago

Nairobi, Kenya World Agroforestry Centre (ICRAF) Full time

The World Agroforestry Centre, is an international institute headquartered in Nairobi, Kenya, and founded in 1978. The Centre specializes in the sustainable management, protection and regulation of tropical rainforest and natural reserves.

Overview
The agroecological systems analyst will contribute to the development, implementation and documentation of analytical and modeling frameworks that integrate biophysical, ecological, and socioeconomic domains to evaluate the impacts of agroecological interventions on livelihood systems and ecosystems. Other responsibilities will be to support the management and co-development of interactive web-based dashboards and databases.
Duties and responsibilities
Research Design & Data Collection:

Support the design of research projects in agroecology with an eye to ensuring they will be able to collect high-quality data to address project goals.
Design surveys and focus group discussion guides for data collection.
Translate questions into digital collection tools such as ODK and Kobo Collect to emphasize question quality, data validation framework and option relevance.
Train enumerators and team members on data collection, interviewing and note-taking for easy translation to viewable data.
Undertake literature reviews and other desk-based data collection methods.
Ensure data are stored in secure and ethical ways.

Data Analysis:

Clean and curate data to enable analysis.
Design data analysis frameworks that connect the available data to the expected results and formulate hypotheses.
Conduct exploratory data analysis to uncover insights in the data that will advise the next course of analysis action.
Analyze data to test hypotheses and create predictive models using R or Python.
Create visualizations and graphs of the data, including using spatial tools such as QGIS.

Database/dashboard development:

Contribute to the development of digital dashboards that allow stakeholders to interact with data, including:
Farmer dashboards with personalized data collected in projects.
Metrics dashboards/databases allowing users to develop monitoring systems using existing metrics.
Project dashboards showing implementation progress or other metrics aimed at donors/investors.

Communications & Stakeholder Engagement.

Interact with multiple actors (farmers, development actors, donors, NGO etc.) to ensure user-centred design of dashboard/database interfaces.
Develop presentations, briefs, data visualizations or other tools to report on data/results to diverse stakeholders, particularly policy makers developing agroecological strategies.
Disseminate research results and original ideas through diverse channels, including giving presentations, attending conferences, or other fora.

Publications & Reporting:

Contribute to peer-reviewed publications, particularly supporting the writing of methods and results sections.
Contribute to project reporting and presentation of results to stakeholders.
Write briefs/info notes on results of data analyses, such as ROI of agroecology, or performance of novel holistic metrics for diverse audiences.

Management & Oversight:

Ensure high-quality data collection in the field through training and supervising teams of enumerators and field technicians.
Manage field work budgets as appropriate the ensure collection of high-quality data and smooth stakeholder engagement.
Supervise junior data analysts and research assistants.

Resource Mobilization:

Contribute to the development of proposals related to agroecology, sustainable food systems monitoring, climate adaptation, or other strategic areas through idea development, writing and contributing data/analyses.

Other Duties as commensurate with the position and assigned by the supervisor.
Education, knowledge and experience

Master’s Degree in Data science, Statistics, or related field such as Agriculture, Environmental Science, Economics, with demonstrated analytical skills.
Minimum of 3 years relevant professional level work experience.
Experience in designing experiments to answer targeted questions and conducting exploratory data analysis.
Strong technical skills in statistical analysis, data modeling, programming languages (R or Python), and machine learning to solve problems/predict patterns in scientific projects, and visualizing results.

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