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.
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.
Own our insurance price estimation system end to end – defining the roadmap, identifying and acquiring new data sources, and moving us from a rules-based engineering solution to a Bayesian statistical model that returns calibrated estimates with confidence intervals even where data is incomplete.
Translate statistical models into concrete product milestones, sequencing the work across incremental launches and collaborating with engineering to get things into production.
Work with rich but messy operational datasets – cleaning, joining, interpreting, and stress-testing them – and make the case for investing in new external sources where the evidence supports it.
Rebuild forecasting infrastructure that is currently limited, supporting capacity planning, patient routing, and network design.
Run counterfactual analyses and empirical studies to validate model designs and inform decisions on ad spend, utilisation, and patient direction.
4–7 years of experience in a quantitative field (Statistics, Economics, Math, Computer Science, Physics, or similar)
Undergraduate degree with strong quantitative rigor (economics, applied math, engineering, computer science, physics, or similar); Master’s or PhD preferred
Advanced fluency in Python or R, including experience working with large, messy datasets and building production-quality analyses
Comfort with at least one programming or analytical tool beyond SQL/Excel (e.g., Python, R, dbt, BI tooling)
Strong statistical intuition and ability to reason from first principles
Experience building production-facing models or decision systems, not just exploratory analyses
Comfort working with large, imperfect real-world datasets such as insurance claims or reimbursement files
A clear, demonstrated history of academic and professional excellence
High ownership and ability to operate in ambiguous, fast-moving environments
Startup-ready mindset: resourceful, hands-on, and energized by ambiguity.
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