
Data Scientist, Product
About this role
The Data Scientist, Product role at Anthropic involves driving data-informed decision-making across the organization to support the creation of safe and beneficial artificial intelligence systems. Based in New York City, San Francisco, or Seattle, this position suits experienced data professionals eager to help shape analytics best practices in a fast-growing environment.
Role Overview
As part of the Data Science and Analytics team, the professional in this position will dive deep into product and user data to derive actionable insights. The work directly informs company strategy and roadmaps while scaling analytics infrastructure to support rapid product iteration.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Anthropic is a public benefit corporation focused on building reliable, interpretable, and steerable artificial intelligence systems that remain safe and beneficial for society. Operating as a single cohesive team, the organization approaches AI research as an empirical science, placing high value on collaboration, impact, and diverse perspectives.
How to Apply
Candidates should submit their application directly through the official Anthropic careers page.
Skills and tech stack
Questions candidates ask
What locations is this role based in?
This position is located in New York City, NY, San Francisco, CA, or Seattle, WA, operating under a hybrid policy requiring at least 25% time in the office.
What is the salary range for the Data Scientist, Product position?
The annual base salary range is $285,000 to $380,000 USD.
How many years of experience are required?
Candidates must have 7 or more years of experience in data science or analytics roles.
Does Anthropic offer visa sponsorship for this job?
Yes, Anthropic sponsors visas for eligible candidates and retains an immigration lawyer to assist with the process.
What technical skills are required?
Applicants need deep expertise in Python, SQL, data visualization tools, causal inference, statistical modeling, and A/B testing frameworks.