Data Scientist, Developer Productivity

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.

As part of our growing Data Science and Analytics team, you'll play an instrumental role in Anthropic's mission of building safe and beneficial AI by driving data-informed decision making across the company. This role sits at the intersection of data science, developer experience, and AI tooling — and offers the unusual opportunity to study frontier AI usage from the inside, with the builders themselves as your users.

You'll define how Anthropic understands and improves developer productivity — both through classic software engineering effectiveness measures and through the emerging challenge of understanding AI-augmented development workflows. You'll own the quantitative foundation for how Anthropic's engineers build: what slows them down, what accelerates them, where tooling investments pay off, and how AI-assisted development is changing the shape of engineering work. Your analyses will directly inform infrastructure priorities, tooling roadmaps, and how we think about scaling engineering output as Anthropic grows.

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.

Key responsibilities

Minimum qualifications

• Define key metrics, build measurement frameworks, and maintain core reporting to evaluate developer productivity and engineering effectiveness
• Deep dive into product and user data to derive actionable insights, size opportunities, and influence roadmaps through clear recommendations
• Develop hypotheses and apply rigorous causal inference methods — controlled experiments, synthetic controls — to make actionable recommendations
• Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions
• Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes
• Present complex analyses and recommendations to both technical and non-technical stakeholders
• Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration as our products grow
• Working expertise with Python and SQL
• Working expertise with data visualization tools
• Hands-on experience with experimental design, causal inference, statistical modeling, and A/B testing frameworks
• Strong written communication and presentation skills
• Track record of translating complex data into clear, actionable insights for both technical and business stakeholders

Deadline to apply: None. Applications are reviewed on a rolling basis. The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

• 7+ years of experience in data science or analytics roles
• Direct experience working with developer productivity, infrastructure, performance, or platform teams in rapidly scaling environments
• Deep understanding of distributed systems, cloud infrastructure, and performance engineering, with experience analyzing large-scale system metrics
• Experience applying experimental design and causal inference methods in high-scale technical environments

$275,000—$370,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.