The opportunity
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…
What you'll do
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
What they're looking for
- + years of hands-on data science experience, ideally in infrastructure, performance, or platform contexts
- Direct experience with developer productivity, developer experience, or internal tooling, at any scale
- Experience measuring the adoption or impact of AI-assisted workflows, or: other tooling where the ground truth was contested
- A track record of building an experimentation or causal-inference practice in: an org that didn't already have one
- Prior staff-level or tech-lead scope: setting direction for other ICs and owning a domain's data strategy end to end
