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Evidence Action

Non-profit scaling evidence-based health interventions

Director – Monitoring - Learning, And Evaluation, AI Access Initiative

Data SciencePosted on Nov 9
Full-time$142k - $162k /yr

About the Job

Requirements

  • At least 10 years of progressive experience in monitoring, learning, and evaluation, including hands-on leadership of a rigorous quantitative function that enables data-driven decisions in global health or development.
  • Expertise in evaluation design and analysis, including statistically rigorous impact evaluations, process evaluations, and mixed-methods approaches, with 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 giving clear, frequent feedback.
  • A clear understanding of and experience using artificial intelligence in the monitoring, learning, and evaluation process.
  • Experience with 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 the tradeoffs.
  • Ability to adjust technical communication for monitoring, learning, and evaluation specialists, funders, government counterparts, and non-technical audiences to drive decisions.
  • Experience building relationships with program teams, partners, and peer organizations, with an external presence through publications, conference talks, or similar platforms.
  • Applicants must be legally authorized to work in the country where they are based.

Responsibilities

  • Define the organization's measurement and evaluation approach across Agriculture, Health, and the Incubator, and maintain rigorous standards from design through dissemination, including standards for acting on weak or emerging evidence, uncertainty, and key assumptions.
  • Test and establish best practices for artificial-intelligence-enabled monitoring, learning, and evaluation approaches, share lessons with the sector, and explore frontier tools and techniques such as large language model-assisted evidence synthesis and agentic analysis pipelines to improve evaluation quality, speed, and cost.
  • Build the monitoring, learning, and evaluation team by designing its structure, recruiting initial hires, and developing them into a high-performing group.
  • Set a clear performance bar and build a culture of active feedback.
  • Provide technical supervision and mentorship for diagnosing and solving complex problems.
  • Assess the evidence base for early-stage programs and advise the Incubator, strengthening rigor and consistency when assessing and selecting interventions.
  • Review existing evidence with the Incubator team and stress-test hypotheses.
  • Advise the Incubator team on program assessment and pilot design, identifying how program designs align with or depart from existing evidence and ensuring pilots are measurable and focused on key learning goals.
  • Identify evidence gaps and develop stage-appropriate learning plans for interventions with limited evidence.
  • Review outcome and cost-effectiveness models developed by the Incubator team, pressure-testing how evidence informs assumptions and whether results are realistic.
  • Design measurement protocols and data-collection processes for live programs that reflect operational realities, and ensure rigorous iteration as Agriculture and Health programs scale.
  • Build rapid experimentation, including A/B testing of artificial-intelligence tool features and delivery approaches, into live programs so teams can learn and iterate quickly.
  • Identify the highest-value questions across the portfolio and ensure monitoring, learning, and evaluation findings reach program and country teams making decisions on the ground.
  • Own the monitoring, learning, and evaluation budget and resourcing plan, make tradeoffs aligned with organizational priorities, and secure buy-in and funding to sustain the function.
  • Represent the organization's evidence externally by communicating methods, findings, and learning to funders and partners, inviting and addressing external feedback, and publishing monitoring, learning, and evaluation work as public goods.

Desired Qualifications

  • Experience with 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.

About the Company

Evidence Action

Non-profit scaling evidence-based health interventions

Evidence Action reduces poverty and improves health by scaling proven, cost-effective interventions in low-income regions. Its programs include Deworm the World, which works with governments to run national school-based deworming programs, and Dispensers for Safe Water, which installs chlorine dispensers at rural water sources to provide safe drinking water. The organization supports governments, communities, and other nonprofits by delivering programs that have been rigorously evaluated for impact and cost efficiency, relying on charitable donations, grants, and partnerships to fund scaling. Unlike groups that offer generic aid, Evidence Action emphasizes evidence-based results and tight use of resources to achieve measurable improvements. The goal is to reach millions more people with proven health and poverty-reduction interventions and to demonstrate clear, scalable impact.

Company size
Not listed
Company stage
Seed
Total funding
$120K
Headquarters
Washington DC, District of Columbia
Founded
2012

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