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 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.
You will own measurement and analysis for new logo acquisition through activation, expansion, and retention for a rapidly scaling, consumption-based AI platform. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing data science function at a pivotal moment in the company's growth.
Key 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
Minimum qualifications
- 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 c
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