Data Scientist, Growth

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2 id="about-the-role" data-lfile-container="abae6479-3fac-48d3-99d5-050459213e8f" data-pm-slice="1 3 []">About the role</h2> <p>The Growth team's job is to keep Anthropic on the exponential by getting more people and companies using and paying for Claude. Growth Data Scientists partner closely with growth product managers, engineers and marketers on experimentation, opportunity sizing, strategic analysis, metrics and personalization.</p> <p>We're hiring for two focus areas within this posting. Tell us in your application which one fits you best, or if you're excited about both:</p> <ul> <li><strong>Growth generalist:</strong> a strong generalist across marketing and product who drives experimentation, opportunity sizing, marketing measurement and strategic analysis.</li> <li><strong>Personalization:</strong> building and evaluating the models and experiments that pick the right message, offer or product experience for each user, across lifecycle marketing, onboarding and in-product experiences.</li> </ul> <p>We work in an AI-native way. Much of our analysis, reporting and experiment work is done with Claude as a collaborator, and we expect you to push that further.</p> <h2 id="responsibilities">Responsibilities</h2> <p>Across both focus areas, you will:</p> <ul> <li>Design and analyze A/B tests and quasi-experiments across activation, retention and monetization, and turn the results into a clear call on what to ship</li> <li>Define systems of metrics and guardrails that tie short-term wins to long-term value</li> <li>Present analyses and recommendations to technical and non-technical audiences, leading with the decision and keeping the numbers in support</li> </ul> <p>In the Growth generalist focus, you will also:</p> <ul> <li>Size opportunities and make the case for what Growth should work on next</li> <li>Measure the incrementality of paid and lifecycle marketing with holdouts, geo experiments and other causal methods, and turn the results into budget and channel recommendations</li> <li>Tie acquisition and lifecycle programs to long-term value (LTV, CAC and payback)</li> </ul> <p>In the Personalization focus, you will also:</p> <ul> <li>Build and ship targeting and personalization models that decide which users get which message, offer or experience, working closely with growth product managers and engineers</li> <li>Design the tests that evaluate and iterate on our personalization strategy, including holdouts, bandits and off-policy estimates where A/B tests aren't practical</li> </ul> <h2 id="you-may-be-a-good-fit-if-you-have">You may be a good fit if you have</h2> <ul> <li>7+ years of experience in data science, with significant time on growth, marketing, lifecycle or personalization problems</li> <li>Deep expertise in experimental design and causal inference, including A/B testing at scale, holdouts and quasi-experimental methods when randomization isn't available</li> <li>Experience analyzing growth funnels: acquisition, activation, retention and monetization</li> <li>Proficiency with Python and SQL</li> <li>Clear written communication, and a track record of recommendations that changed priorities or what a team built</li> <li>A bias for action and comfort in ambiguous, fast-moving environments</li> <li>An interest in Anthropic's mission of building safe and beneficial AI</li> </ul> <p>For the Growth generalist focus, we're also looking for:</p> <ul> <li>Experience measuring marketing: incrementality testing, holdouts or geo experiments, and LTV/CAC for paid or lifecycle programs</li> </ul> <p>For the Personalization focus, we're also looking for:</p> <ul> <li>Hands-on experience building and shipping machine learning models that drive user-facing decisions (targeting, ranking, recommendation or personalization)</li> </ul> <h2 id="strong-candidates-may-also-have">Strong candidates may also have</h2> <ul> <li>Experience defining activation metrics and lifecycle measurement for a product-led business</li> <li>Experience with experimentation platforms and meta-analysis across experiment portfolios</li> <li>Background at consumption-based, multi-product companies serving both consumers and enterprises</li> <li>Experience using LLMs or coding agents in your own analytical workflow</li> <li>Experience with marketing mix modeling, geo experiments or synthetic controls</li> <li>Experience running contextual bandits, uplift models or similar decision systems in production</li> <li>Experience with lifecycle or CRM personalization (email, push, in-product messaging) and its measurement problems, such as global holdouts and interference between campaigns</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below. </p> <p>For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the