Anthropic
Data Scientist, GTM
$285K–$380K
New York City, NY +3 · Full-time · Posted May 31, 2026
- Location
- New York City, NY
San Francisco, CA
New York City, NY
San Francisco, California, United States - Experience
- 5+ years preferred; required years correlate with internal job level requirements.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; field relevant to the role as demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; Anthropic says it will make reasonable efforts to obtain a visa for an offer recipient and provides immigration counsel.
About the job
Measure and analyze commercial customer lifecycle performance to guide go-to-market strategy for Anthropic’s consumption-based AI platform.
What you'll do
- Define metrics and reporting; analyze commercial and user data to inform strategy and product roadmaps.
- Apply causal inference, statistical models, optimization, and simulations to make recommendations and support commercial decisions.
- Present findings to stakeholders and help establish data practices and analytics infrastructure.
What they ask for
- 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.
- Ability to translate complex data into actionable insights for technical and business audiences.
- Strong written communication and presentation skills.
Nice to have
- 5+ years of experience in data science or analytics roles.
- Experience in B2B commercial analytics, consumption-based revenue models, AI/ML products, large language models, or developer tools.
Pay
$285,000 — $380,000 USD (annual salary; base salary)
Tools
Python, SQL
AI focus
The role analyzes commercial performance for a consumption-based AI platform; experience with AI/ML products, large language models, or developer tools is preferred.
Model-written summary · the original posting is authoritative