You will design the methodology behind core platform capabilities: estimating causal drivers of delivery performance; modeling the cost and carbon impact of tariff policy changes; forecasting input costs as functions of commodity prices, exchange rates, and macroeconomic conditions; and building scenario simulation tools that quantify tradeoffs across cost, risk, and sustainability.

This is not a traditional research economist role. You will work directly with ML engineers to implement your methods in production systems, validate models against real customer data, and iterate based on observed outcomes. Your causal frameworks will inform automated sourcing recommendations, your tariff models will power interactive scenario planning tools, and your carbon accounting methodology will help companies meet important environmental protection goals and regulatory requirements.

We are particularly interested in candidates with backgrounds in industrial organization (firm behavior, market structure, supply networks) and environmental economics (carbon pricing, emissions accounting, regulatory impact analysis). You should be fluent in both applied econometrics (causal methods, bayesian estimation, forecasting) and modern ML methods, and comfortable translating academic rigor into practical systems that operate at scale.We are looking for an Economist to lead the development of causal inference frameworks, policy impact models, and economic scoring systems embedded in our platform.