Director, Monitoring, Learning, and Evaluation – AI Access Initiative

Evidence Action

. Define 2AI's approach to measurement and evaluation across Agriculture, Health, and the Incubator .

Posted 11/10/2026full-timeRemote • United States, Canada, Kenya, IndiaLead💰 $142,000 - $162,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Monitoring, Learning, and Evaluation (MLE) with a focus on quantitative analysis and AI integration. Proven ability to build high-performing teams, establish rigorous standards, and communicate complex findings to diverse stakeholders.

Highest-signal resume keywords

Monitoring, Learning, And Evaluation (MLE)Quantitative AnalysisAI Integration In MLETeam Building And LeadershipEvaluation Design And Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistically Rigorous Impact EvaluationsProcess EvaluationsMixed-Methods ApproachesQuantitative Modeling Under UncertaintyData Collection ProcessesA/B TestingEvidence SynthesisOutcome And Cost-Effectiveness EstimatesPerformance Standards SettingTechnical Supervision

Soft Skills

Technical CommunicationRelationship-BuildingComfort With AmbiguityFeedback Culture DevelopmentDecision-Making Under Uncertainty

Tools & Technologies

RPythonStataAI ToolsData Analysis Software

Industry Keywords

Global HealthDevelopmentLow- And Middle-Income CountriesProgram AssessmentEvidence Gaps

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Define 2AI's approach to measurement and evaluation across Agriculture, Health, and the Incubator
Set and maintain rigorous MLE standards from design through dissemination
Establish standards for acting on weak or emerging evidence, uncertainty, and key assumptions
Test and create industry best practices for AI-enabled MLE approaches
Explore frontier AI tools and techniques, including LLM-assisted evidence synthesis and agentic analysis pipelines
Design the MLE team structure, recruit first hires, and develop a high-performing team
Set performance standards and build a culture of active feedback
Provide technical supervision and mentorship on complex problems
Advise the Incubator on program assessment and pilot design
Review existing evidence and stress-test hypotheses
Identify evidence gaps and develop stage-appropriate learning plans
Pressure-test outcome and cost-effectiveness models and key assumptions
Design measurement systems, protocols, and data collection processes for live Agriculture and Health programs
Build rapid experimentation, including A/B testing of AI tool features and delivery approaches
Identify high-value portfolio questions and ensure findings reach program and country teams
Own the MLE budget and resourcing plan
Secure buy-in and funding for the MLE function
Communicate methods, findings, and learning to funders and partners
Publish MLE work as public goods

Requirements

What you’ll need

10+ years of progressive experience in monitoring, learning, and evaluation, including hands-on leadership of a rigorous quantitative function enabling data-driven decisions in global health or development
An advanced degree in economics, statistics, public health, or another quantitative field, or a comparable mix of advanced training and technical experience; a PhD is optional
Expertise in evaluation design and analysis, including statistically rigorous impact evaluations, process evaluations, and mixed-methods approaches
Experience managing large-scale quantitative evaluations from design through dissemination
Fluency in interpreting existing evidence and translating it into informed program decisions
Track record of building teams and developing senior technical talent, including setting performance standards and giving clear, frequent feedback
Clear understanding of and experience in using AI in the MLE process
Quantitative modeling under uncertainty, including outcome and cost-effectiveness estimates for early-stage programs
Comfort with ambiguity and experience setting up new systems, structures, and standards where none exist
Ability to make tough choices among imperfect options and communicate tradeoffs
Technical communication skills for MLE specialists, funders, government counterparts, and non-technical audiences
Relationship-building with program teams, partners, and peer organizations, with external presence through publications, conference talks, or similar platforms
Applicants must be legally authorized to work in the country where they are based
Evidence Action cannot sponsor or take over sponsorship of an employment visa
Experience with R, Python, Stata, or similar tools is a plus
In-the-field experience in low- and middle-income countries is nice to have
Experience supporting government partners with data-led decisions on program implementation is nice to have

Benefits

Comp & perks

Comprehensive benefits package
Health insurance
Vision insurance
Dental insurance
Retirement plans
Generous and flexible leave options
Other employee perks
International travel (~15-20%)