Data Scientist, Product at Anthropic

Hybrid - New York City, NY; San Francisco, CA; Seattle, WA

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Anthropic’s Data Science & Analytics team seeks a Product Data Scientist to drive data‑informed decision making across the organization, leveraging advanced analytics and causal inference to shape product strategy and operations. The role involves deep analysis of product and user data, building measurement frameworks, developing statistical models and optimization solutions, and presenting findings to both technical and business stakeholders to influence roadmap and operational decisions.

Salary

USD 285,000 - 380,000

Requirements

Skills

  • 7+ years of experience in data science or analytics roles
  • Deep expertise with Python, SQL, and data visualization tools
  • Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments
  • Highly effective written communication and presentation skills
  • A track record of translating complex data into clear, actionable insights for both technical and business stakeholders
  • A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress
  • A passion for the company’s mission of building helpful, honest, and harmless AI
  • Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem
  • Bachelor’s degree or equivalent combination of education, training, and/or experience
  • A field relevant to the role as demonstrated through coursework, training, or professional experience

Responsibilities

  • Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations
  • Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls – and analyze the results in order make actionable recommendations
  • Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions
  • Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success
  • Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes
  • Present complex technical analyses and recommendations to both technical and non-technical stakeholders
  • Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow

Technologies

PythonSQLData visualization toolsCausal inference methodsExperimental designA/B testing frameworksStatistical modelingOptimization frameworksSimulations

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