Economist II

Expedia Group•Austin, TX

1d•$119,000 - $166,500

About The Position

Expedia Group's Economics and Experimentation Science team is seeking an Economist to support causal inference and marketplace measurement on its global travel platform. The work addresses high-stakes questions such as measuring incrementality in two-sided marketplaces, estimating the long-term value of customer behavior signals, and evaluating product changes with quasi-experimental methods when A/B testing is insufficient. Findings inform decisions balancing traveler experience, partner economics, and platform health. This individual-contributor role provides opportunities to contribute to method development under the guidance of senior team members and to develop technical expertise in causal inference. The Economist partners with ML engineers, data scientists, analysts, and product leaders, translating rigorous econometric analysis into insights for product decisions. The position is available in Seattle or Austin. The total cash range in either location is $119,000 to $166,500, with potential to increase pay up to $190,500 based on ongoing, demonstrated, and sustained performance. Starting pay varies according to factors including location, budget, knowledge, skills, and experience, and ranges may change. Expedia Group offers benefits and perks intended to support employees and their families, including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and IATAN membership.

Requirements

Master's degree in Economics, Applied Economics, Econometrics, Statistics, or a related quantitative field, or a bachelor's degree with equivalent related professional experience.
At least 2 years of relevant professional experience.
Foundation in microeconomic theory and applied econometrics, with developing ability to apply appropriate causal inference techniques.
Exposure to applying econometric methods to real-world business problems; technology, e-commerce, or marketplace experience is preferred.
Working knowledge of causal inference tools such as instrumental variables, difference-in-differences, regression discontinuity, synthetic controls, panel data methods, and experimental design.
Proficiency in Python or R and strong SQL skills for querying and processing large-scale, noisy, real-world data.
Ability to explain econometric methodology, identifying assumptions, and business implications to non-technical audiences.
Ability to work collaboratively and autonomously in a fast-paced business environment.

Nice To Haves

PhD in Economics, Applied Economics, Econometrics, Statistics, or a related quantitative field.
Coursework, research, or working papers involving applied economics, econometrics, or quantitative methods.
Exposure to marketplace or platform economics, including two-sided markets, network effects, dynamic pricing, or general equilibrium modeling.
Exposure to metalearners, doubleML, or causal forests.
Familiarity with modern AI/ML workflows and LLM-assisted analysis tools.

Responsibilities

Build econometric models, including panel data methods, difference-in-differences, regression discontinuity, and instrumental variables, to measure elasticity, supplier behavior changes, and customer lifetime value in settings involving endogeneity, simultaneity, and platform dynamics.
Support development of long-term impact measurement approaches that quantify sustained effects on customer retention, partner engagement, and marketplace equilibrium beyond short-term experiment metrics.
Assist in designing and executing causal measurement frameworks for marketplace interventions, including geo experiments, difference-in-differences, synthetic controls, and instrumental-variable approaches.
Partner with ML teams to integrate causal inference into production systems, supporting ranking algorithms, pricing optimization, and recommendation engines with econometrically validated insights.
Translate econometric findings into clear, well-organized analyses and visualizations to support product decisions, and present results to stakeholders.
Collaborate with data scientists and economists across the organization, contributing to rigorous causal reasoning and experimental design.