Data Scientist, Developer Productivity
Data Scientist partnering with Developer Productivity leadership at Anthropic to define, measure, and improve developer productivity in an AI-first organization. Lead investigations, set metrics frameworks, run experiments on AI tooling, and influence leadership with data in a fast-changing space. Requires strong SQL/Python, ambiguous problem-solving, and 8+ years data science experience.
About the job
Key Responsibilities
- Lead ambiguous, high-stakes investigations into developer productivity in an AI-first organization, such as determining if Claude makes engineers faster and defining what "faster" means.
- Treat findings as provisional in a rapidly changing space; bias toward instrumenting early, gathering broad evidence, and revising priors as data emerges.
- Partner with Developer Productivity engineering leadership to set the measurement and research agenda—what to study, build, or stop.
- Define metrics framework for developer productivity in an AI-augmented org and drive its adoption for tooling and infrastructure decisions.
- Design and run experiments on internal tooling and workflow changes to build causal evidence for productivity impacts.
- Influence engineering, infrastructure, and product leadership with data; push back when data doesn't support narratives.
- Build analytical foundations (pipelines, dashboards, models) hands-on or through partners.
Minimum Qualifications
- Experience writing production-quality SQL and Python (or similar) to build pipelines, dashboards, and models independently.
- Experience as the primary data or analytics voice in ill-defined spaces, helping to define the questions.
- Track record of holding conclusions loosely, favoring instrumentation and evidence over defending priors, and revising views publicly when evidence warrants.
- Experience shaping what an engineering or product team works on (consulted before decisions, not just after measuring outputs).
- Genuine interest in how AI changes software development, with firsthand experience on harder, less-defined aspects.
- Comfort presenting data-backed conclusions to engineers, including when data shows a feature isn't impactful.
Preferred Qualifications
- 8+ years of hands-on data science experience, ideally in infrastructure, performance, or platform contexts.
- Direct experience with developer productivity, developer experience, or internal tooling.
- Experience measuring adoption or impact of AI-assisted workflows or contested tooling.
- Track record building experimentation or causal-inference practice in an organization without one.
- Prior staff-level or tech-lead scope: setting direction for other ICs and owning a domain's data strategy end-to-end.
Compensation and Benefits
- Annual compensation range: $380,000–$460,000 USD (total compensation).
- Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours.
- Minimum education: Bachelor’s degree or equivalent.
- Location-based hybrid policy: Expect staff in offices at least 25% of the time.
Skills
SQL, Python, Data Pipelines, Dashboards, Causal Inference, Experiment Design, Developer Productivity Metrics, Ai-Assisted Workflows
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