Data Scientist, Policy

Anthropic Anthropic · AI Frontier · San Francisco, CA · Data Science & Analytics

Data Scientist focused on policy work, transforming internal product usage and survey data into evidence for policymakers. This role uses AI tools to aggregate public data and develops measurement frameworks for policy communications.

What you'd actually do

  1. Partner with product and business teams across the company to produce supporting analyses and data collateral for specific policy position papers and partnership conversations
  2. Determine which metrics faithfully represent our company to legislators, regulators, and the public
  3. Own the dashboards and pipelines that keep externally-shared numbers consistent, so the Policy team can move quickly without creating discrepancies
  4. 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
  5. Develop measurement frameworks for policy communications, paid media, and public-affairs campaigns

Skills

Required

  • Python
  • SQL
  • data analysis tools
  • working with external and public data sources
  • producing analysis that reaches external audiences such as policy, communications, investor relations, public affairs, or published research
  • Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data
  • translate complex analyses into clear, actionable recommendations for audiences with differing levels of technical fluency

Nice to have

  • 6+ years of hands-on data science experience
  • Direct experience supporting a policy, government affairs, or regulatory team with data and analysis
  • Familiarity with investor-relations or financial-disclosure data standards and the consistency requirements they entail
  • Comfort operating in ambiguous, fast-moving environments where creating clarity and driving progress is part of the role
  • A genuine interest in Anthropic's mission of building safe and beneficial AI

What the JD emphasized

  • Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data