Data Scientist, Safeguards

Anthropic•Seattle, WA

23h•$275,000 - $370,000•Hybrid

About The Position

As an early member of our Safeguards Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by building and scaling a data driven culture from the ground up. In this unique company, technology, and moment in history, your work will be critical to informing our product and commercial strategy as we deploy safe, frontier AI at scale to the world. You will work closely with product, engineering, policy & enforcement to define and measure key company success metrics, analyze user behavior to identify new enforcement opportunities and build a culture of developing and testing hypotheses through experimentation. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to building robust and scalable systems and processes as our company goes through a phase of rapid growth.

Requirements

Expertise in Python, SQL, and data visualization tools.
A bias for action and urgency, not letting perfect be the enemy of the effective.
A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress.
A deep curiosity and energy for pulling the thread on hard questions.
Experience in turning open questions and data into concise and insightful analysis.
Highly effective written communication and presentation skills.
A passion for the company's mission of building helpful, honest, and harmless AI.

Nice To Haves

6+ years of experience in data science or analytics roles, preferably in an infrastructure or operations context.
3+ years of experience deeply embedding in Product teams.
Experience working on safety, anti-abuse, integrity shaped problems.

Responsibilities

Deep dive into user behavior data to provide insights on safety concerns
Define core metrics that measure the team's success. Set goals, build forecasts, monitor performance, and develop actionable reporting
Identify and size opportunities to improve the product, influencing product roadmap through your insights and recommendations
Develop hypotheses on product changes, design controlled experiments, analyze the results, and make recommendations based on impact to key metrics
Build a data driven culture from the ground up by establishing foundational data best practices and making data more accessible across the company