Data Scientist, Developer Productivity

Data Scientist, Developer ProductivitySan Francisco, CA | New York City, NYAbout AnthropicAnthropic'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 RoleYou'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 ResponsibilitiesLead 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 sharpensPartner with Developer Productivity engineering leadership to set the team's measurement and research agenda — what to study, what to build, what to stopDefine the metrics framework for developer productivity in an AI-augmented org, and drive its adoption as the basis for tooling and infrastructure investment decisionsDesign and run experiments on internal tooling and workflow changes; build the causal evidence base for what actually moves productivityInfluence 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 eitherBuild the analytical foundations (pipelines, dashboards, models) yourself or through partners — staying hands-on and close to the work rather than directing from a distanceMinimum QualificationsExperience writing production-quality SQL and Python (or a similar language) to build pipelines, dashboards, and models independentlyExperience serving as the primary data or analytics voice in a space where the questions weren't yet well-defined, and helping define themA track record of holding conclusions loosely — favoring instrumentation and evidence-gathering over defending a prior position, and revising views in public when the evidence warrants itExperience shaping what an engineering or product team worked on, not only measuring what they shipped — being consulted before a decision was made, not just afterGenuine interest in how AI is changing the way software gets built, with some firsthand experience grappling with the harder, less-defined parts of that questionComfort presenting data-backed conclusions to a room of engineers, including when that means saying a built feature isn't moving the needlePreferred Qualifications8+ years of hands-on data science experience, ideally in infrastructure, performance, or platform contextsDirect experience with developer productivity, developer experience, or internal tooling, at any scaleExperience measuring the adoption or impact of AI-assisted workflows, or other tooling where the ground truth was contestedA track record of building an experimentation or causal-inference practice in an org that didn't already have onePrior staff-level or tech-lead scope: setting direction for other ICs and owning a domain's data strategy end to endLogisticsMinimum education: Bachelor's degree or an equivalent combination of education, training, and/or experienceRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionLocation-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.How We're DifferentWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.Come Work With Us!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.