Data Scientist, Developer Productivity

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.