Data Scientist, GTM

Anthropic•San Francisco, CA

4d•$285,000 - $380,000•Hybrid

About The Position

As part of Anthropic's growing Data Science & Analytics team, this role is instrumental in driving data-informed decisions across the commercial customer lifecycle. It sits at the intersection of sales operations and statistical analysis, working across multiple segments and products. The role partners with analytics engineers, fellow data scientists, and go-to-market leadership to translate complex commercial data into actionable strategy. The Data Scientist will own measurement and analysis for the entire customer lifecycle, from new logo acquisition through activation, expansion, and retention for a rapidly scaling, consumption-based AI platform. This role is expected to contribute to shaping the norms and best practices of a growing data science function.

Requirements

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

Nice To Haves

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