Senior/Staff Economist
About the Company:
World is a network of real humans, built on privacy-preserving proof-of-human technology, and powered by a globally inclusive financial network that enables the free flow of digital assets for all. It is built to connect, empower, and be owned by everyone.
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 and NY 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
Apply Now
Apply Now
World is a network of real humans, built on privacy-preserving proof-of-human technology, and powered by a globally inclusive financial network that enables the free flow of digital assets for all. It is built to connect, empower, and be owned by everyone.
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 and NY 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
Apply Now
Apply Now