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

Evidence Action

1d•$142,000 - $162,000

About The Position

The AI Access Initiative (2AI), incubated at Evidence Action and in the process of becoming an independent organization, is seeking a Director of Monitoring, Learning, and Evaluation (MLE) to build and lead its MLE function from the ground up. The role will set the evidence standard for 2AI’s live Agriculture and Health programs and interventions developed by its Incubator, and will be accountable for the technical rigor of measurement, learning, and reporting. The Director will establish the team, systems, and protocols, while exploring how frontier AI can make MLE faster, less costly, and more rigorous. The Director will define MLE strategy and standards across the portfolio, lead and develop the MLE team, advise the Incubator on intervention assessment and pilot design, and build measurement systems for live programs. The position will promote rapid experimentation, identify high-value learning questions, ensure evidence informs operational decisions, manage the MLE budget and resourcing plan, and communicate methods and findings externally as public goods. Approximately 30% of the role is expected to support the Incubator. The role is open to candidates based in the US, Canada, Kenya, or India. It may require international travel of approximately 15–20% to partner countries. Applicants must be legally authorized to work in their country of residence; visa sponsorship is not available. The US salary range is $142,000–$162,000 per year. Compensation and benefits vary by location and role specifications.

Requirements

10 or more years of progressive experience in monitoring, learning, and evaluation, including hands-on leadership of a rigorous quantitative function supporting data-driven decisions in global health or international development.
An advanced degree in economics, statistics, public health, or another quantitative field, or a comparable combination 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.
A track record of building teams and developing senior technical talent, including setting performance standards and providing clear, frequent feedback.
Strategic judgment, including the ability to identify problems early and ask questions that clarify context.
Clear understanding of and experience using AI in the MLE process.
Ability to undertake quantitative modeling under uncertainty, including outcome and cost-effectiveness estimates for early-stage programs.
Comfort with ambiguity and experience establishing systems, structures, and standards where none exist.
Bias to action and ability to make difficult choices among imperfect options and communicate tradeoffs.
Ability to communicate technical material effectively to MLE specialists, funders, government counterparts, and non-technical audiences to support decisions.
Ability to build relationships with program teams, partners, and peer organizations, with external engagement through publications, conference talks, or similar platforms.
Legal authorization to work in the country where the candidate is based; the organization is unable to sponsor or take over sponsorship of an employment visa.

Nice To Haves

Experience using R, Python, Stata, or similar tools.
In-the-field experience in low- and middle-income countries.
Experience supporting government partners with data-led decisions on program implementation.

Responsibilities

Define 2AI’s measurement and evaluation strategy and standards across Agriculture, Health, and the Incubator, maintaining rigor from study design through dissemination.
Establish minimum standards for decisions based on weak or emerging evidence, uncertainty, and key assumptions.
Test and develop best practices for AI-enabled MLE, including exploring LLM-assisted evidence synthesis and agentic analysis pipelines, and share learnings with the sector.
Design the MLE team structure, recruit initial team members, develop them into a high-performing group, set performance standards, and foster active feedback.
Provide technical supervision and mentorship to address complex problems.
Advise the Incubator on assessing and selecting new interventions, including reviewing existing evidence and stress-testing hypotheses.
Partner with the Incubator on program assessment and pilot design, identify alignment with or departures from existing evidence, and ensure pilots are measurable and focused on key learning goals.
Identify evidence gaps and develop learning plans appropriate to program stage, recognizing the limited evidence base for AI-enabled interventions.
Review and pressure-test outcome and cost-effectiveness models, including their evidence, assumptions, and plausibility.
Design measurement protocols and data collection processes grounded in operational realities for live programs, and support rigorous iteration as programs scale.
Build rapid experimentation, including A/B testing of AI tool features and delivery approaches, into live programs.
Identify high-value questions across the portfolio and ensure MLE findings reach program and country teams making operational decisions.
Own the MLE budget and resourcing plan, make tradeoffs aligned with organizational priorities, and secure support and funding for the function.
Represent 2AI’s evidence externally by communicating methods, findings, and learning to funders and partners, addressing external feedback, and publishing MLE work as public goods.