Monitoring, Evaluation & Learning Evidence Scientist

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Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Design and lead monitoring, evaluation, and learning (MEL) frameworks for all IAAI activities, including theories of change, results frameworks, and impact indicators. Build rigorous quantitative and qualitative evaluation methods to measure adoption, usage, outcomes, and real-world impact. Turn findings into learning loops with technical teams and leadership, integrate MEL into research and deployment, and produce evidence reports, dashboards, and knowledge products for decision-makers.

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Mid level

Key Responsibilities

•Design and lead MEL strategy, theories of change, results frameworks, and performance indicators aligned to IAAI’s mission.
•Implement evaluation methodologies to measure adoption, usage, outcomes, and impact of AI-enabled advisory systems.
•Manage evidence generation workflows: data collection, validation, analysis, and synthesis to maintain credibility and quality.
•Facilitate learning loops and reflection sessions to support adaptive management and continuous improvement.
•Produce evaluation reports, learning briefs, dashboards, and evidence products for leadership, governments, donors, and partners.

Key Requirements

•Lead the Institute’s monitoring, evaluation, and learning (MEL) strategy aligned with IAAI’s mission and international best practices.
•Develop and maintain theories of change, results frameworks, and performance indicators for both technical performance and real-world impact.
•Design and implement quantitative and qualitative methodologies to assess adoption, usage, outcomes, and impact.
•Oversee data collection, validation, analysis, and synthesis to ensure high-quality, credible evidence and accountability.
•Use learning loops to translate MEL findings into actionable insights and support adaptive management and inclusive equity indicators.

Experience:8-10 yearsImpact evaluationDevelopment programsInnovationAI-enabled programsMulti-country implementation

Education:Master's

Skills:Evidence-based decision-makingData analysisStakeholder collaborationAdaptive learning

Tech Stack:AIData scienceImpact evaluation

Company Brief

MBZUAI

Website

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MBZUAI

6 months ago

Monitoring, Evaluation & Learning Evidence Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Mid level

Key Responsibilities

•Design and lead MEL strategy, theories of change, results frameworks, and performance indicators aligned to IAAI’s mission.
•Implement evaluation methodologies to measure adoption, usage, outcomes, and impact of AI-enabled advisory systems.
•Manage evidence generation workflows: data collection, validation, analysis, and synthesis to maintain credibility and quality.
•Facilitate learning loops and reflection sessions to support adaptive management and continuous improvement.
•Produce evaluation reports, learning briefs, dashboards, and evidence products for leadership, governments, donors, and partners.

Key Requirements

•Lead the Institute’s monitoring, evaluation, and learning (MEL) strategy aligned with IAAI’s mission and international best practices.
•Develop and maintain theories of change, results frameworks, and performance indicators for both technical performance and real-world impact.
•Design and implement quantitative and qualitative methodologies to assess adoption, usage, outcomes, and impact.
•Oversee data collection, validation, analysis, and synthesis to ensure high-quality, credible evidence and accountability.
•Use learning loops to translate MEL findings into actionable insights and support adaptive management and inclusive equity indicators.

Experience:8-10 yearsImpact evaluationDevelopment programsInnovationAI-enabled programsMulti-country implementation

Education:Master's

Skills:Evidence-based decision-makingData analysisStakeholder collaborationAdaptive learning

Tech Stack:AIData scienceImpact evaluation

Company Brief

MBZUAI

Website