Marketplace Scientist

We’re building a platform that makes it delightfully simple for people to find, book, and afford care across virtual and in-person visits, prescriptions, labs, imaging, and more.

As a Marketplace Scientist at General Medicine, you’ll work directly with our Chief Economist to design the algorithms, mechanisms, and models that underpin the matching, pricing, and insurance estimation systems at the heart of the business. From understanding patient preferences to forecasting prices across complex insurance networks, your work will determine how the marketplace functions and scales.

We’re looking for someone with a deep understanding of machine learning, marketplace design, and optimization —someone excited to turn elegant theory into practical, production-ready systems.

We especially value candidates who are comfortable reasoning from economic primitives; who understand how supply, demand, and incentives interact; and who can move fluidly between empirical work, and algorithm design. Familiarity with machine learning or prediction is helpful, especially for price estimation, but not the central focus.

You should be equally comfortable analyzing data, building models, and experimenting in production. You’ll work on mechanisms that must both reflect real-world behavior and scale operationally.

• Design and evaluate matching algorithms that connect patients to clinicians for excellent outcomes and short time-to-care.

• Build and test models that estimate prices, incorporating noisy, incomplete, or strategically selected data.

• Construct demand and supply forecasts that support routing, capacity planning, and network design.

• Work with rich operational datasets—cleaning, joining, interpreting, and stress-testing them—and help identify and integrate new data sources.

• Collaborate with engineering to deploy model-driven mechanisms into production environments.

• Run simulations, counterfactual analyses, and empirical studies to validate designs and inform marketplace strategy.

• PhD in Economics, Operations Research, Computer Science, Applied Math, or a related quantitative field.

• Familiarity with both economic modeling and machine learning or statistical prediction

• Strong coding skills in Python and SQL, and comfort working with data

• Prior business or startup experience is a plus, especially in analytics, pricing, or data-driven operational roles.

• Startup-ready mindset: resourceful, hands-on, and energized by ambiguity.