Main responsibilities

• Attend a two-week training to get familiarized with the evidence synthesis research topic and protocol, data extraction tool, and quality control processes.
• Screen publications against predefined inclusion and exclusion criteria, using human-assisted machine learning methods.
• Extract relevant data on adaptation effectiveness following a structured protocol, including causal evidence of effectiveness, study design, data collection methods, ensuring comprehensive coverage of gray and peer-reviewed literature.
• Ensure data quality and consistency, verifying data accuracy, validity, and reliability.
• Contribute to the refinement of the data extraction system by providing feedback on workflow improvements and reporting technical issues.
• Participate in regular team meetings and report progress via internal communication tools (e.g., Slack, Google Sheets, Notion).
• Contribute to data analysis and co-author research outputs, based on interest.

Requirements

Required qualifications and experience

• Advanced Degree in Agronomy, Agroecology, Environmental Sciences, Livestock Science, Landscape Ecology, Policy, Agricultural or Development Economics, International Agricultural Development, Climate Change, or related field.
• Proven experience with climate change adaptation projects in the agricultural sector
• Familiarity with systematic reviews, meta-analyses, or evidence synthesis methods in climate adaptation and/or agriculture.
• Understanding of methodologies used in adaptation assessments, including evaluations and impact assessments. Proven experience with econometrics/quantitative analysis is preferred.
• Proven ability to critically assess research methodologies and identify sources of bias in climate adaptation literature.
• Strong organizational and analytical skills, with exceptional attention to detail.
• Ability to work independently and collaboratively in a cross-cultural, remote team environment under tight deadlines.
• Excellent written and oral communication skills in English. Knowledge of French is an asset.
• Proficiency in data analysis and documentation tools, including Microsoft Office (Excel), Google Sheets, and Slack. Experience with R/Python is a plus.