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.
Data Scientist, GTM at Anthropic
Hybrid - San Francisco, CA, United States
More jobs at AnthropicSalary
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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