Description
As a Data Scientist, Developer Productivity, you will 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 will 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 will 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.
Key responsibilities include defining key metrics, building measurement frameworks, and maintaining core reporting to evaluate developer productivity and engineering effectiveness; deep diving into product and user data to derive actionable insights, size opportunities, and influence roadmaps through clear recommendations; developing hypotheses and applying rigorous causal inference methods , controlled experiments, synthetic controls , to make actionable recommendations; investigating anomalies, conducting root cause analyses, and providing data-driven insights to guide priorities and inform decisions; building statistical models, optimization frameworks, and simulations to automate decision-making and operational processes; presenting complex analyses and recommendations to both technical and non-technical stakeholders; establishing foundational data practices and helping scale our analytics infrastructure to support rapid iteration as our products grow.
Minimum qualifications include 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, and a track record of translating complex data into clear, actionable insights for both technical and business stakeholders.