Data Scientist, Policy

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As one of our first Data Scientists dedicated to policy work, you will play a key role in ensuring Anthropic's work is understood by policymakers around the world. You'll sit at the intersection of data science and public affairs, transforming internal product usage and survey data into clear, accurate, and consistent evidence the Policy team can use to inform its positions, demonstrate Anthropic’s relevance to policymakers, and measure the impact of its work. Your analyses will directly inform how legislators, regulators, and the public understand Anthropic's footprint and contribution. This is a highly cross-functional role that is foundational to Anthropic's mission of bringing powerful AI to the world in a way that benefits humanity. Key responsibilities Partner with product and business teams across the company to produce supporting analyses and data collateral for specific policy position papers and partnership conversations Determine which metrics faithfully represent our company to legislators, regulators, and the public Own the dashboards and pipelines that keep externally-shared numbers consistent, so the Policy team can move quickly without creating discrepancies Use AI tools to aggregate news, policy developments, and other public data sources to track trends and provide a clear picture of the evolving regulatory landscape Develop measurement frameworks for policy communications, paid media, and public-affairs campaigns Design and implement policy analyses to support internal position development or measure the impact of policy implementation for external audiences Minimum qualifications Proficiency in Python, SQL, and data analysis tools, with experience working with external and public data sources Experience producing analysis that reaches external audiences such as policy, communications, investor re lations, public affairs, or published research Applied causal inference using quasi-experimental designs (e.g.