Primary record

Data Scientist, Product

Anthropic Indexed employerNew York City, NY; San Francisco, CA; Seattle, WA · San Francisco, California, United States
Source-hosted applyChecked 18m agoInternship
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Workplace

hybrid

Employment

Internship

Published

Aug 25, 2026

Closes

No date supplied

The role

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 and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world.

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 <li

Requirements

Department: Data Science & Analytics