Data Science Manager, Supply

<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 class="heading"><strong>About the role</strong></h2> <p>As part of our growing Data Science and Analytics team, you will lead the Supply DS pillar and play an instrumental role in our company's mission of building safe and beneficial AI. Anthropic is completely supply-constrained — how we allocate compute is one of the highest-leverage decisions we make, and today that allocation is tied only minimally to user outcomes like retention and LTV. You'll change that.</p> <p>Embedded with our infrastructure leadership, you'll build the quantitative foundation — observational analyses, synthetic experiments, and optimization frameworks — for how Anthropic allocates its most scarce resource. In this unique company, technology, and moment in history, your work will directly shape how frontier AI reaches the world at scale.</p> <h2 class="heading"><strong>Responsibilities:</strong></h2> <ul> <li>Build and grow the Supply Data Science team, raising the bar for talent and shaping the culture of the pillar</li> <li>Design a testing framework — observational and synthetic — to quantify how different inputs affect compute allocation outcomes</li> <li>Tie compute allocation decisions to downstream user outcomes (retention, LTV, revenue) so we stop optimizing in a vacuum</li> <li>Measure and improve how AI affects our own developer productivity inside Anthropic</li> <li>Represent the DS organization in senior leadership forums with our CTO and his staff, owning the narrative on compute and supply</li> <li>Uplevel reporting, metrics, and shared understanding of the supply area across the company</li> <li>Establish foundational data practices and scale analytics infrastructure to support rapid iteration as we grow</li> </ul> <h2 class="heading"><strong>You may be a good fit if you have:</strong></h2> <ul> <li>10+ years of technical IC / tech-lead-manager experience in data science, analytics, or operations research</li> <li>A strong Operations Research background — you think natively in terms of optimization, constrained allocation, and queueing</li> <li>Direct experience embedded with high-performing engineering teams, not at arm's length from the system</li> <li>Experience working on highly complex systems with many interacting components (ad networks, credit-card processing, marketplace matching, routing, etc.)</li> <li>A track record of owning and presenting analyses in high-pressure executive forums — you can take a punch and still move the room</li> <li>Deep expertise with Python, SQL, and data visualization tools</li> <li>A passion for the company's mission of building helpful, honest, and harmless AI</li> </ul> <h2 class="heading"><strong>Strong candidates may also have:</strong></h2> <ul> <li>Experience with causal inference methods applied to operational decisions (synthetic controls, geo-experiments, switchbacks)</li> <li>Experience designing experimentation platforms from scratch, not just using them</li> <li>Exposure to AI/ML products, large language models, or large-scale inference systems</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.&nbsp;</p> <p>For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p></div><div class="title">Annual Salary:</div><div class="pay-range"><span>$450,000</span><span class="divider">&mdash;</span><span>$565,000 USD</span></div></div></div><div class="content-conclusion"><h2><strong>Logistics</strong></h2> <p><strong>Minimum education: </strong>Bachelor’s degree or an equivalent combination of education, training, and/or experience</p> <p><strong>Required field of study:&nbsp;</strong