Anthropic
Data Scientist, Growth
$285K–$380K
New York City, NY +3 · Full-time · Posted Mar 23, 2026
- Location
- New York City, NY
Seattle, WA
San Francisco, CA
San Francisco, California, United States - Experience
- 7+ years of data science experience; the posting also says minimum years will correlate with internal job level requirements.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field of study demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic says it will make every reasonable effort to obtain a visa.
About the job
Partners with Growth teams to improve Claude acquisition, activation, retention, and monetization through experimentation, measurement, strategic analysis, and—depending on focus—personalization models.
What you'll do
- Design and analyze experiments, define metrics and guardrails, and recommend what to ship.
- For the generalist focus, size opportunities and measure marketing incrementality to guide budgets and channel choices.
- For the personalization focus, build and evaluate models that select user messages, offers, or product experiences.
What they ask for
- 7+ years of data science experience, with significant time on growth, marketing, lifecycle, or personalization problems.
- Expertise in experimental design and causal inference, including A/B testing, holdouts, and quasi-experimental methods.
- Experience analyzing acquisition, activation, retention, and monetization funnels.
- Proficiency with Python and SQL.
Nice to have
- Experience with experimentation platforms, meta-analysis, or marketing mix modeling.
- Experience using LLMs or coding agents in an analytical workflow.
Pay
$285,000 — $380,000 USD (base salary)
Tools
Python, SQL, Claude
AI focus
Uses Claude as a collaborator in analysis, reporting, and experimentation; the personalization focus builds and evaluates machine learning models for user-facing decisions.
Model-written summary · the original posting is authoritative