Key Details

Compensation: $50 - $100 per hour based on experience

Contract: Part-time, potentially transitioning to full-time

Location: Fully remote

Reports to: Director of Research

Start date: ASAP

The Role

As an Scenarios & Modeling Economist, you will build and own Windfall Trust's quantitative modeling capability. You will be the technical authority behind everything the organization publishes: the person who chooses the methods, assembles the data, writes or directs the code, and defends the results to external economists, peer reviewers, and government analysts.

The immediate priority is a scenario modeling platform that translates narrative AI futures into quantified economic parameters, propagates those shocks through household microdata, and produces distributional results policymakers can act on: who loses income, by how much, what it costs the fiscal system, and how candidate policy responses perform under each scenario.

But the role is deliberately broader than any single platform. Over time you may be designing cross-country composite indices, building reduced-form macro shock generators, evaluating fiscal tax outcomes, stress-testing welfare systems, and evaluating which modeling approach fits which question. We are looking for someone with range across quantitative methods, not a specialist in one technique.

This is a build role rather than a maintenance one. Windfall has no existing modeling infrastructure, so you will be making foundational methodological choices.

The Moment

Artificial intelligence is poised to be one of the most significant economic disruptors in modern history — potentially on par with the Industrial Revolution or the rise of the internet. Like those transformations, it will reshape how wealth is created, how work is organized, and who benefits. The question is: are we ready?

Right now, the answer is no. Governments do not have a plan in the likely event that AI leads to large-scale labor displacement — let alone policies to address the broader shifts that may follow as AI becomes embedded across the economy. While much attention has been paid to AI’s capabilities and risks, far less effort has been devoted to developing concrete policy responses to this transformation.

Windfall’s core mission is to ensure the economic benefits of transformative AI are broadly shared — not captured by a small number of actors. This involves increasing awareness of the scale of potential economic disruption, while also helping to develop and surface practical policy responses through research, tools, and collaboration.

Governments will need a concrete game plan. They will have to answer tough questions: How can the economic gains from AI be more broadly distributed? How should education systems evolve in response to an AI-driven economy? What changes are needed to labor market policies and social safety nets? Many of our economic systems will need to be re-examined in light of the scale and speed of AI-driven change.

About Windfall Trust

Windfall Trust is a global policy accelerator preparing society for the economic disruption of transformative AI. Our mission is to ensure that the economic benefits of advanced AI are broadly shared rather than captured by a few. We do this by raising awareness, building coalitions, and strengthening the evidence base for policy action.

Our research program spans several connected workstreams. The Scenarios Program convenes economists, AI researchers, policymakers, and civil society leaders to explore plausible economic futures. A Preparedness Index will assess countries' exposure to AI-driven economic disruption and their capacity to absorb it. The Policy Atlas maps policy options for governments preparing for AI's economic consequences.

Responsibilities

You will:

• Own the design and delivery of Windfall's scenario modeling platform: defining the economic primitives that parameterize scenarios, mapping aggregate shocks onto occupations using the AI exposure literature, and propagating them through quantitative modeling to produce distributional and fiscal outputs.

• Design a macroeconomic modeling approach to describing a wide range of economic scenarios that can be leveraged to support economic preparedness.

• Lead methodology for Windfall's country-level work, including index construction: indicator selection, standardization, weighting, uncertainty quantification, and defending design choices.

• Evaluate and select methods, models, and data sources across countries with very different statistical infrastructure, making principled judgments about when to use microsimulation, reduced-form estimation, structural modeling, or simpler transparent arithmetic.

• Simulate and cost policy responses under each scenario, including transfer programs, capital taxation, and social insurance reforms, and present honest sensitivity analysis rather than false precision.

• Co-author Windfall's flagship publications, translating technical results into analysis that policymakers and journalists can understand without misrepresenting the underlying uncertainty.

• Set the standards for reproducibility, documentation, and quality control across Windfall's quantitative work, so that every published number can be traced and defended.

About You

You are a senior economist with demonstrated range. You have built or substantially contributed to more than one kind of quantitative model of economic outcomes, and you have well-formed views about when each approach is appropriate. You might have that range from a national treasury or central bank, a legislative scoring body, an international financial institution, a policy modeling shop, or an applied academic career, and we are open to all of those backgrounds.

You are comfortable being the most technical person in the room and the person accountable when external economists push back. You can explain why a modeling choice was made, what it assumes, and where it breaks, and you would rather publish a caveated result than an impressive-looking one you cannot defend.

You are pragmatic about data. You know how to extract defensible conclusions from messy inputs, and you flag clearly what the data cannot support.

You write well. Windfall's outputs will go to national policymakers, economists, and international institutions, and the modeling is only useful if the write-up earns their trust.

You do not need prior expertise in AI economics specifically, but you should be able to engage critically with the AI exposure and adoption literature and form your own views on where it is strong and where it overreaches.

Experience

Strong candidates will bring most of the following:

• An advanced degree in economics or a closely related quantitative field (a PhD is valuable but not required if compensated by applied modeling experience), plus 5+ years of applied quantitative modeling of economic outcomes.

• Hands-on experience across multiple modeling approaches, for example: tax-benefit or dynamic microsimulation, reduced-form empirical estimation, macro or structural modeling, composite index construction, or fiscal costing and revenue estimation.

• A publication or public-analysis record where your methodology survived external scrutiny: peer review, official scoring, government clearance, or equivalent.

• Experience with distributional analysis of labor market shocks, technology adoption, or structural economic transitions.

• Familiarity with the AI-and-labor literature (occupational exposure indices, augmentation versus automation framings, adoption evidence) is an advantage but can be learned quickly by the right candidate.

• Comfort working in a fast-moving, remote, international team, with the self-direction that requires.

Experience presenting technical work to non-technical policy audiences is a strong advantage.

Process

We run a structured hiring process designed to be thorough but respectful of your time.

1. Application review. We review applications on a rolling basis, but will prioritize applications submitted before August 15th.

2. Screening interview (30 minutes).

3. Technical exercise interview (approximately 30 minutes).

4. Final interview (approximately 45 minutes).

5. Reference check.

The starting date will be determined together with the successful applicant.

Not sure if you’re qualified? Apply anyway.

We actively encourage applications from candidates with diverse backgrounds and experiences.