Key Details

Compensation: $160,000 - $210,000

Contract: Part-time or full-time

Location: Fully remote

Reports to: CEO & Director of Research

Start date: ASAP

The Role

As Lead Economist, you will own Windfall's core research products and be the economic authority behind what we publish. We are interested in experts with an extensive background in labor economics, fiscal or welfare analyses, quantitative modeling, or development experience.

Depending on your strengths, that work could span ownership of our existing Policy Atlas, an upcoming AI Observatory (a cross-country index of AI economic exposure and absorption capacity), empirical research on crucial measurement data during an AI transition, or evaluating the assumptions behind our scenario modeling. In each case, ownership means the full arc: methodological design, data and analysis, drafting, external review, and standing behind the results when treasuries, central banks, and academic economists push back.

Windfall's research infrastructure is young, so you will be making foundational choices about methods, data, and framing, and living with their consequences. You will work directly with the CEO and Director of Research, alongside a small research team and external technical partners, and your work will feed directly into engagement with national governments and international institutions.

Responsibilities

You will

Own the methodology and delivery of one or more of Windfall's flagship research products: a v2 of a Policy Atlas, a Preparedness Index, a Scenarios Lab, or a Scenarios Report.

Design and defend cross-country index methodology, including indicator selection, standardization, or weighting, and uncertainty.

Translate narrative AI scenarios into quantified economic parameters and work with modeling partners to propagate them through household microdata into distributional and fiscal results.

Analyze labor market resilience: which occupations and workers are exposed, how transitions historically play out, and what determines whether economies absorb or amplify a technology shock.

Assess fiscal and welfare system resilience across countries at very different income levels, including how safety nets, tax bases, and transfer systems would perform under sustained labor market disruption.

Cost and evaluate candidate policy responses under each scenario, from social insurance reform to transfer programs to capital taxation.

Author and co-author Windfall's publications. Represent Windfall's research externally, in review by academic economists, in engagement with government analysts and international institutions, and at convenings with senior policy audiences.

Set standards for rigor, reproducibility, and documentation across Windfall's quantitative work, so every published number can be traced and defended.

About You

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.