Data Scientist, Economic Insights & Research

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

The Economic Insights & Research team sits at the intersection of economic research and Stripe's core business. We use Stripe's unparalleled real-time view of the global economy, spanning millions of businesses, industries, and geographies, to produce original economic research that shapes how Stripe understands the world and how the world understands Stripe. Our work informs major product and business decisions, contributes to public discourse on economic trends, and helps Stripe fulfill its mission of growing the GDP of the internet.

As a Data Scientist on the Economic Insights & Research team, you will conduct original, rigorous economic research using both Stripe's internal datasets and public data sources, producing findings that directly drive major technical and strategic decisions across the company. You'll work like an economist embedded in one of the world's most data-rich companies, bringing academic-grade rigor to real-world questions that matter at scale.

• Design and execute deep economic research projects using Stripe's internal transaction data alongside public datasets (e.g., BLS, Census Bureau, Federal Reserve, OECD, and academic data sources)
• Apply rigorous econometric and statistical methods, including causal inference, time series analysis, panel data modeling, and quasi-experimental designs, to answer consequential questions about the economy and Stripe's role in it
• Translate economic research findings into clear, actionable insights that influence major product, policy, and business decisions across Stripe
• Build and maintain robust, reproducible data pipelines and analysis that enable ongoing economic research at scale
• Partner closely with cross-functional teams, including Product, Finance, Policy, and Communications, to identify research questions that advance Stripe's strategic priorities
• Present research findings to senior internal audiences and external stakeholders, including policymakers, academics, and the media
• Collaborate with external researchers, academic institutions, and policy organizations to advance the rigor and reach of Stripe's economic research agenda

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

• PhD or Master's degree in Economics, or a closely related field (e.g., Applied Economics, Econometrics, Public Policy, or Economic Sociology)
• Strong foundation in econometrics and applied statistics, including comfort with causal inference frameworks (difference-in-differences, instrumental variables, regression discontinuity, etc.)
• Proficiency in Python and SQL for data analysis, modeling, and pipeline development
• Experience conducting independent research using large, messy datasets, from hypothesis generation through to publication-quality findings
• Experience working independently on multiple concurrent research projects with high standards for rigor and reproducibility

• Experience in an applied research role at an institution known for economic analysis (e.g., Federal Reserve, CBO, World Bank, IMF, or a top academic or policy institution)
• Familiarity with macroeconomic indicators, labor market dynamics, price indices, or cross-border trade and commerce
• A track record of publishing or publicly presenting economic research (working papers, policy briefs, media coverage, etc.)
• Experience with time series modeling, index construction, or economic nowcasting methodologies