Sr. Economist; ML, Industrial Organization or Environmental Economics

About the Team & Role

The AI Economics Team at Auger is building an AI and agentic-powered market intelligence system that enables our customers to forecast supply chain risk, optimize sourcing decisions, and respond to global disruptions in real time. 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.

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  are looking for an Economist to lead the development of causal inference frameworks, policy impact models, and economic scoring systems embedded in our platform.

• PhD in economics, with coursework in econometrics and causal inference
• Experience with causal inference methods with ML: DML or causal forests, instrumental variables, regression discontinuity, synthetic control.
• Strong programming skills in Python; comfort working in production codebases
• Ability to translate business questions into well-specified causal or economic frameworks
• Experience with at least one of: policy impact analysis, market structure modeling, carbon/emissions accounting, or trade economics
• Clear written and verbal communication; ability to explain methodology to engineers, product teams, and business stakeholders

• Familiarity with ML interpretability methods (SHAP, partial dependence, calibration)
• Experience with carbon accounting frameworks (GHG Protocol, ISO 14064) or regulatory regimes (CBAM, CSRD, EPA reporting)
• Background in demand forecasting, supply chain, procurement, or logistics economics
• Experience deploying economic models in production systems or decision-support tools