Job Title: Computational Economist Report to: The Head of Data Science Location: Greater San Francisco Bay Area (available to work on-site 1+ day a week) Role Overview We are hiring a Computational Economist to study how AI is reshaping work, and to model it. r.Potential helps companies bring AI into their organizations, so the questions span scales: the global labor market, an individual corporation, and the people and roles inside it. You will work with our Global Labor Graph, the growing body of data we are assembling on the world's workforces and the companies that employ them, to study where automation displaces work and where it augments it, how skills and roles shift, and what a company's AI adoption does to its people over time. This is a role for someone who treats an economic question as something you build a model and mine data to answer. You will design agent-based and other simulations that run from the labor market down to the level of individual roles and workers, develop methods to pull signal out of messy real-world data, and turn the results into something companies can act on. It is hands-on: you will write and run your own work in Python and Databricks. We want someone who cares about the subject matter as deeply as the methods. Key Responsibilities • Build agent-based and other simulations of labor-market dynamics, modeling how workforces shift as AI adoption spreads across occupations, industries, and geographies, and use them to produce defensible counterfactuals. • Think creatively about what signal is mineable in the data, prototype it quickly, and validate it against ground truth. • Work across both the Global Labor Graph and company-level data, including financial reports, to model company and market workforce composition. • Build and operate your own models and pipelines in Python and DataBricks. Required Qualifications • Education: Bachelor's degree in computational economics, economics, or a closely related quantitative field with a strong computational or ML component required. Master's or PhD preferred. • Depth over tenure: We are hiring for genuine depth in the field rather than years in industry, so early-career candidates with the right training are encouraged to apply. We are looking for someone with creativity and a real passion for the subject, who has done original work with data (a thesis, research, publications, or substantial projects) and can reason fluently about the methods they use. • Technical/Functional Expertise: Strong Python and machine learning with hands-on data-science practice. Comfort working in Databricks (or a clear path to it) and with large datasets, some of them messy. Working knowledge of both SQL and NoSQL databases. Our stack includes Postgres, Elasticsearch, and Neo4j. • Domain Knowledge: Real grounding in economics and/or finance, with genuine curiosity about how businesses and labor markets work across countries. Preferred Skills • Agent-based modeling, structural estimation, or other simulation and computational-economics methods at scale. • Published or unpublished research in labor economics, the economics of AI and automation, or a closely related area. • Comfort with large public economic and labor datasets, and with multi-country, multi-standard data. • Excellent communication, able to explain methods and findings to non-specialists. The anticipated annual base salary range for this position is included in between $150k - $225k based on experience + equity. Compensation varies based on a variety of factors including, but not limited to, experience, education, key skills, and geographic location. Benefit offerings for full-time employment include medical, dental, vision, term life and AD&D insurance, short-term and long-term disability, additional voluntary benefits, commuter benefits, wellness plans, and a 401k plan or a non-qualified deferred compensation plan. Available paid leave includes Personal Time Off (PTO) on an accrual basis up to 152 hours a year, 10 Paid Holidays, 1 Community Service Day, and up to 6 weeks of Paid Parental Leave. PTO and holiday hours are prorated based on hire date within the calendar year.