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Ofir Reich spent 6 years doing math in the military, before spending another 2 in tech startups – but then made a sharp turn to become a data scientist focussed on helping the global poor.

At UC Berkeley’s Center for Effective Global Action he helps prevent tax evasion by identifying fake companies in India, enable Afghanistan to pay its teachers electronically, and raise yields for Ethiopian farmers by messaging them when local conditions make it ideal to apply fertiliser. Or at least that’s the hope – he’s also working on ways to test whether those interventions actually work.

Why dedicate his life to helping the global poor?

Ofir sees little moral difference between harming people and failing to help them. After all, if you had to press a button to keep all of your money from going to charity, and you pressed that button, would that be an action, or an inaction? Is there even an answer?

After reflecting on cases like this, he decided that to not engage with a problem is an active choice, one whose consequences he is just as morally responsible for as if he were directly involved. On top of his life philosophy we also discuss:

  • The benefits of working in a top academic environment
  • How best to start a career in global development
  • Are RCTs worth the money? Should we focus on big picture policy change instead? Or more economic theory?
  • How the delivery standards of nonprofits compare to top universities
  • Why he doesn’t enjoy living in the San Francisco bay area
  • How can we fix the problem of most published research being false?
  • How good a career path is data science?
  • How important is experience in development versus technical skills?
  • How he learned much of what he needed to know in the army
  • How concerned should effective altruists be about burnout?

Keiran Harris helped produce today’s episode.

Highlights

It was called the reproducibility crisis because people would try to reproduce the same research and, obviously, if they test my hypothesis that I just engineered somehow and they’re going to try and recreate the experiment, they’re not going to get the same result. It won’t replicate. So it’s not reproducible research. I prefer to call it the published results being false crisis.

It’s not that it’s about something intangible, reproducibility. It’s about science not producing the truth, which is a big problem.

When I thought about it long and hard, I figured out that there are many cases where you can’t really even state what’s the action, what’s the inaction, right? I mean, if I had to press a button to keep all my money from going to charity and I press that button, is that action or inaction, right?

I think, to be slightly controversial, I think people in development are a little high on how much development experience is important, especially for my type of job. I know that if today, I had to hire another data scientist, I’d go for the better person with better data skills instead of the person with more development experience.

There is something to be said for development experience. I wouldn’t take a person that I think would never set foot in a developing country, would never understand these considerations, would be annoyed if things are not working perfectly well anywhere outside the private sector.

But I think it’s overestimated how much development experience is important for this position. I think that the data skills are the more important part.

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About the show

The 80,000 Hours Podcast features unusually in-depth conversations about the world's most pressing problems and how you can use your career to solve them. We invite guests pursuing a wide range of career paths — from academics and activists to entrepreneurs and policymakers — to analyse the case for and against working on different issues and which approaches are best for solving them.

The 80,000 Hours Podcast is produced and edited by Keiran Harris. Get in touch with feedback or guest suggestions by emailing [email protected].

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