About the Company:

World is building a real human network designed to accelerate people in the age of AI. As bots and autonomous agents reshape the internet, people, institutions, and applications need a trusted way to confirm who is a real human while preserving privacy. Our products make this possible: the Orb verifies real people, World ID proves it privately, and World App enables and distributes the new applications made possible by this technology. Together, they form a new layer for AI internet.

We’re one of the fastest-growing networks in tech. More than 17 million people across 160 countries have verified with World ID, and we complete over 350,000 verifications each week. World App is already among the most used wallets globally. Developers are integrating World ID to build safer online experiences and create spaces where real people can participate, earn, and be recognized in ways AI simply can’t replicate.

World was founded in 2019 and launched globally in 2023. We are more than 400 people across hardware, software, AI, cryptography, mobile engineering, and global operations. Our teams come from OpenAI, Tesla, SpaceX, Apple, Google, Stripe, Meta, Coinbase, Palantir and MIT Media Lab. We’re backed by leading investors, including a16z, Khosla Ventures, Bain Capital Crypto, Blockchain Capital, Variant, Tiger Global, and Coinbase Ventures, as well as prominent operators and founders across fintech and AI.

World has been featured on the cover of TIME Magazine, highlighted in Fast Company’s Next 5 in Fintech, and explored in a Bloomberg deep dive. The New York Times, Bankless and TechCrunch have all recognized our progress in identity, cryptography, AI, and global-scale hardware deployment. Our leadership is also named to the Time AI 100. Learn more about the newest product launches from our Unwrapped event.

This role is with Tools for Humanity

About the team

The Economics team at Tools for Humanity partners across Product, Market Operations, Marketing, Engineering, Policy, and Finance to guide key strategic decisions with rigorous, scalable econometric work and experimentation. We use causal inference, experimentation, and structural modeling to understand behavior, optimize incentives, and ensure the World project grows sustainably and equitably. Our work touches growth experiments, incentive design, marketing measurement, and policy—helping World allocate resources efficiently and responsibly as it scales to billions of users globally.

About the role

We’re hiring an applied Economist at the senior or staff level to help World and Tools for Humanity make better, faster decisions using economic reasoning and empirical rigor. You’ll design and analyze experiments, build structural models, and apply causal inference at global scale—informing how we grow the network, design incentives, and evaluate our decisions and operations. This is an applied role focused on turning ambiguity into clear, data-driven recommendations that shape the future of World’s economic and policy design.

In this role, you will:

• Frame and scope economic questions that matter for growth, incentives, and policy—choosing the right empirical approach for each.

• Design, analyze, and interpret experiments that inform real-world choices about user incentives, marketing, and market operations.

• Develop quasi-experimental studies (DiD, event studies, synthetic control, IV, RD, matching) that isolate causal effects in complex, real-world settings.

• Estimate structural models (e.g., discrete choice/demand, dynamic discrete choice, switching, or market design) to simulate counterfactuals and evaluate alternative strategies.

• Use causal ML approaches (DML/meta-learners, causal forests, uplift) to uncover heterogeneous effects and improve policy targeting.

• Work efficiently at data scale: use Python and SQL to automate workflows, handle large datasets, and produce reproducible analyses others can easily rerun.

• Communicate findings clearly: author decision memos and present results that quantify trade-offs, uncertainty, and implications for business and policy.

• Strengthen measurement quality: define metrics, detect interference or novelty effects, and establish guardrails that ensure robust inference.

About you:

• You have a PhD in Economics, Econometrics, or a closely related field.

• It is a plus if you have some post-PhD experience applying econometrics to consequential decisions in industry, tech, consulting, or policy. For the Staff-level role: 4+ years post-PhD experience are required.

• You have deep expertise in at least two of the following areas:

  • Observational causal inference

  • Experimentation

  • Structural modeling

• You have a strong command of Python (pandas/numpy; statsmodels or scikit-learn; PyMC a plus) and SQL for empirical work.

• You have the ability to explain complex economic and statistical ideas simply and precisely.

• You have a practical, collaborative approach—balancing rigor with speed to deliver impact at scale.

• The following are a plus: Experience with structural demand estimation (logit/mixed logit/BLP), dynamic discrete choice, or two-sided/platform problems; exposure to causal ML (meta-learners, uplift, causal forests), Bayesian methods, or time-series analysis where relevant; a track record of influencing major product, marketplace, or policy decisions through empirical work; prior experience with blockchain data.

Pay transparency statement (for CA based roles):
The reasonably estimated salary for this role at TFH ranges from $205,000 - $285,000, plus a competitive long term incentive package. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, TFH offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, vision, 401(k) plan and match, life insurance, flexible time off, commuter benefits, professional development stipend and much more!

By submitting your application, you consent to the processing and internal sharing of your CV within the company, in compliance with the GDPR