You are a senior economist with real range. You have worked seriously in at least two of: labor economics, development economics, fiscal or welfare policy, and applied quantitative modeling, and you can move credibly between them. You might come from a national treasury or central bank, an international financial institution, a legislative scoring body, a policy research institute, or an applied academic career.
You are comfortable being the most technical person in the room and accountable when external economists push back. You can explain what a method assumes, where it breaks, and why you chose it anyway, and you would rather publish a caveated result than an impressive-looking one you cannot defend.
You think globally. Much of the existing AI-and-economics conversation is about white-collar work in rich countries. Windfall’s remit runs from advanced economies to countries where the binding constraints are informality, thin tax bases, and weak social protection.
You are pragmatic about data. This work involves imperfect survey microdata, cross-country sources with inconsistent coverage, and questions where the honest answer includes a range. You know how to extract defensible conclusions from messy inputs and how to say clearly what the data cannot support.
You write well. Windfall’s outputs go to treasuries, central banks, and international institutions, and the analysis is only useful if the write-up earns their trust.
You do not need prior expertise in AI economics specifically, though it is a strong advantage. You do need to engage critically with the AI exposure and adoption literature and form your own views on where it is strong and where it overreaches.
Strong candidates will bring most of the following:
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An advanced degree in economics or a closely related field, plus 8+ years of applied economic research or policy analysis; a PhD is valuable but can be substituted by an exceptional applied track record.
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Demonstrated senior-level work in at least two of: labor economics, development economics, public finance, or social protection and welfare policy.
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A publication or public-analysis record where your methodology survived external scrutiny: peer review, official scoring or costing, government clearance, or equivalent.
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Experience working across countries at different income levels, or within international institutions, is a strong advantage.
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Familiarity with the AI-and-labor literature (occupational exposure indices, augmentation versus automation framings, adoption evidence) is a strong bonus but can be built quickly by the right candidate.
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Experience presenting technical work to senior non-technical audiences: ministers, boards, funders, or the press.
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Comfort working in a fast-moving, remote, international team, with the self-direction that requires, and willingness to travel internationally.