Requirements
Requires a Master's degree in economics, statistics, social sciences, public policy, data science, or a related field, with a preference for a PhD or equivalent applied research experience in impact evaluation or development economics. Must have 8-10 years of progressive experience in MEL or evidence generation, with demonstrated expertise in quantitative and qualitative evaluation methodologies and impact assessment.
Description
Job description / Role
Job Type
Full Time
Job Location
Abu Dhabi, UAE
Nationality
Any Nationality
Salary
Not Specified
Gender
Not Specified
Arabic Fluency
Not Specified
Job Function
Finance, Business Analysis & Consulting
Company Industry
Non-profit / NGO / Social Services
Application open:
Full-time
MBZUAI is seeking a highly qualified Monitoring, Evaluation, and Learning (MEL) / Evidence Scientist to design, implement, and oversee rigorous monitoring, evaluation, and learning frameworks across all IAAI activities. The role is responsible for ensuring systematic evidence generation, robust impact measurement, and continuous learning to inform research, deployment, and policy decisions related to AI-enabled agricultural advisory systems.
Key responsibilities
Monitoring, evaluation, and learning strategy
Design and lead the Institute’s monitoring, evaluation, and learning (MEL) strategy in alignment with IAAI’s mission and international best practices.
Develop and maintain theories of change, results frameworks, and performance indicators capturing both technical performance and real-world impact of AI-enabled advisory systems.
Ensure MEL frameworks support evidence-based decision-making across research, deployment, and policy activities.
Evidence generation and impact measurement
Design and implement rigorous quantitative and qualitative methodologies to assess adoption, usage, outcomes, and impact of Institute-supported interventions.
Oversee data collection, validation, analysis, and synthesis to ensure high-quality, credible evidence.
Ensure impact measurement approaches meet internal standards and external accountability requirements.
Learning and adaptive management
Establish structured learning loops that translate monitoring and evaluation findings into actionable insights.
Facilitate learning and reflection sessions with technical teams, partners, and leadership to support adaptive management.
Support continuous improvement of models, deployment strategies, and operational practices based on evidence.
Integration with research and deployment
Collaborate closely with research, technical assistance, and safety teams to integrate MEL considerations into research design, pilots, and scale-up plans.
Provide evidence-based inputs to model refinement, risk assessments, and policy-relevant decision making.
Gender and inclusion integration
Work with the Gender and AI Expert to ensure MEL frameworks include gender-disaggregated and equity-focused indicators.
Assess differential impacts on women and marginalized groups and recommend adjustments to improve inclusivity and effectiveness.
Reporting and knowledge products
Produce high-quality evaluation reports, learning briefs, dashboards, and evidence products for MBZUAI leadership, governments, donors, and partners.
Ensure findings are communicated clearly and appropriately to both technical and non-technical audiences.
Academic qualifications required
Master’s degree in economics, statistics, social sciences, public policy, data science, or related field.
PhD or equivalent applied research experience in impact evaluation, development economics, or a related discipline will be preferred.
Professional experience required
Essential:
Minimum of eight (8) to ten (10) years of progressive experience in monitoring, evaluation, and learning (MEL) or evidence generation within development, research, innovation, or impact-driven programs.
Demonstrated expertise in designing and applying quantitative and qualitative evaluation methodologies, including impact assessment, outcome measurement, and adaptive learning approaches.
Proven ability to generate, analyze, and synthesize evidence to inform strategic decision-making, program improvement, and accountability to stakeholders.
Preferred:
Experience working with digital, data-driven, or AI-enabled programs, particularly in complex or multi-country implementation contexts.
Exposure to research and innovation environments, including collaboration with technical or scientific teams.
Experience drafting, contributing to, or supporting patents, technical disclosures, or intellectual property documentation related to research or innovation outputs.
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