GiveWell is a research organization that identifies and funds cost-effective giving opportunities, focusing on global health and well-being. Our work is funded by tens of thousands of donors who rely on our research to inform their giving. We’ve grown from directing $1.5 million in 2010 to directing more than $400 million in 2025.

Summary

GiveWell is seeking exceptional Senior Researchers to join our Cross-Cutting team—which is responsible for tackling thorny methodological questions, pressure-testing our conclusions, and ensuring research quality as we scale. You'll work on problems that span all of GiveWell's grantmaking areas and shape how we think about cost-effectiveness, uncertainty, and impact.

This role is ideal for researchers who thrive on variety and complexity: one month you might be developing frameworks for comparing health interventions to poverty alleviation programs, the next you could be designing a "lookback" study to assess whether our past grants achieved their intended impact, and after that you might be figuring out how to incorporate local field insights into our cost-effectiveness models.

As part of our research team, you will have an outsized influence on our funding decisions and help us save and improve lives on a global scale.

The Role

GiveWell’s research team aims to find and fund the most cost-effective giving opportunities in global health and development. While our grantmaking teams are focused on funding programs in their specific areas (malaria, vaccines, nutrition, water, livelihoods, and new areas), the cross-cutting team addresses research questions that span across different areas of our work.

In this role, you'll shape and execute a research agenda that brings rigor and creativity to questions like:

Hard research questions

• How should we value averting a death versus improving health outcomes versus increasing income? (Our "moral weights" problem)
• How do we estimate burden of disease and population when underlying data sources contradict each other or are unreliable?
• How should we set our cost-effectiveness bar over time when funding and spending vary unpredictably year to year?
• How should we advise donors on whether to give now or later?
• How should we account for high levels of uncertainty in our cost-effectiveness estimates? When does uncertainty change a funding decision?
• How should we model spillover effects—do health programs affect income, and vice versa? Are we consistent in how we handle this across program areas?
• What discount rate should we use?
• How concerned should we be that the organizations we fund are diverting healthcare workers from government systems? What about other unintended consequences of our grantmaking?

Verification and learning

• Are our grants actually achieving what we predicted? How do we know?
• What can intensive "lookbacks" on past grants teach us about where our models are systematically wrong?
• We've seen potential issues like caseload inflation in malnutrition programs, unreliable chlorination coverage data, and questions about bed net distribution accuracy—how do we catch these problems earlier?
• How do we systematically incorporate field insights, monitoring data, and external feedback into our work?
• Which types of external feedback actually change our minds?
• Where are the blind spots in our research that we haven't even identified yet?
• How do we design M&E systems that tell us whether grants are working, not just whether grantees are reporting what we asked for?
• How accurate are our forecasts? Where are we well-calibrated versus overconfident? How can we improve our forecasting skills?

Building research infrastructure

• How do we train new researchers to be productive within weeks, not months, as we scale from 50 to 100+ people?
• What AI tools could reduce research time by 40%+ without sacrificing quality?
• Can we simplify our cost-effectiveness models for routine grants while preserving rigor for novel or high-stakes decisions?
• How do we build field networks in our top countries that give us "ground truth" on what's actually happening with our grants?
• What would it look like to have easy-to-use databases of M&E data, burden estimates, and lookback findings that any researcher could query?

Unlike our grantmaking teams that develop deep expertise in specific program areas, Cross-Cutting researchers see across all of GiveWell's work. You'll collaborate with every team, spot inconsistencies in how we're making decisions across areas, and serve as an internal check on ou