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
You'll partner with Developer Productivity engineering leadership to define what "developer productivity" means in an AI-first org and to set the strategy for how Anthropic measures, understands, and improves it. This is a space where the playbook doesn't exist yet: AI-assisted development is reshaping how engineers work faster than anyone can measure, and last quarter's answer is already suspect. You'll decide which questions are worth asking, build the evidence to answer them, and stay ready to revise when the ground shifts again.
You'll own the data strategy end-to-end: which metrics earn the org's trust, which investments to push for, which assumptions to challenge — including your own. The space rewards people who hold conclusions loosely, instrument early, and update fast when the data disagrees with the narrative. This role sits at the intersection of data science, developer experience, and frontier AI, with Anthropic's own teams as your users.
Key responsibilities
- Lead ambiguous, high-stakes investigations where the question isn't yet well-formed — from "is Claude making engineers faster?" to "what does 'faster' even mean here?"
- Treat findings as provisional in a space that changes month to month. Bias toward instrumenting first, collecting evidence broadly, and revising the team's priors as the picture sharpens
- Partner with Developer Productivity engineering leadership to set the team's measurement and research agenda — what to study, what to build, what to stop
- Define the metrics framework for developer productivity in an AI-augmented org, and drive its adoption as the basis for tooling and infrastructure investment decisions
- Design and run experiments on internal tooling and workflow changes; build the causal evidence base for what actually moves productivity
- Influence engineering, infrastructure, and product leadership with data. Push back when the data doesn't support the prevailing narrative, and say so plainly when it doesn't support yours either
- Build the analytical foundations (pipelines, dashboards, models) yourself or through partners — staying hands-on and close to the work rather than di
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