Data Scientist, GTM at Anthropic

Hybrid - San Francisco, CA, United States

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As part of our growing Data Science & Analytics team, you will play an instrumental role in Anthropic's mission of building safe and beneficial AI — this time by driving data-informed decisions across the commercial customer lifecycle. This role sits at the intersection of fast-moving sales operations and rigorous statistical analysis. You will work across multiple segments and products, partnering with analytics engineers, fellow data scientists, and go-to-market leadership to turn complex commercial data into actionable strategy.

Salary

USD 285,000 - 380,000

Requirements

Skills

  • Proficiency in Python, SQL, and data visualization tools
  • Expertise in experimental design, causal inference, statistical modeling, and A/B testing, particularly in high-scale technical environments
  • Demonstrated ability to translate complex data into clear, actionable insights for both technical and business audiences
  • Strong written communication and presentation skills
  • Ability to work effectively in fast-moving, ambiguous environments — comfortable creating structure and driving progress where neither yet exists
  • 5+ years of experience in data science or analytics roles
  • A strong track record in multi-segment, multi-product B2B sales or commercial analytics, especially with consumption-based revenue models
  • Experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem
  • Genuine interest in Anthropic's mission of developing safe and beneficial AI

Responsibilities

  • Define key metrics, build measurement frameworks, and maintain core reporting to evaluate GTM success across segments and products
  • Analyze commercial and user data to surface actionable insights, size opportunities, and influence roadmaps and go-to-market strategy
  • Develop hypotheses and apply rigorous causal inference methods — controlled experiments, synthetic controls — to make clear, actionable recommendations
  • Investigate anomalies, conduct root cause analyses, and provide data-driven guidance on priorities and decisions
  • Build statistical models, optimization frameworks, and simulations to support and automate commercial decision-making processes
  • Present analyses and recommendations to both technical and non-technical stakeholders, including GTM leadership
  • Establish foundational data practices and help scale analytics infrastructure to support rapid product and commercial iteration

Technologies

PythonSQLdata visualization toolsexperimental designcausal inferencestatistical modelingA/B testingAI/ML productslarge language models

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