Data Scientist, Supply

Key Responsibilities

• Build and run testing frameworks — observational and synthetic — to quantify how different inputs affect compute allocation outcomes
• Connect compute allocation decisions to downstream user outcomes (retention, lifetime value, revenue)
• Partner closely with infrastructure engineers, product, and research to instrument systems, measure what matters, and ship operational changes
• Develop the metric hierarchies, dashboards, and reporting that turn supply decisions into shared understanding across the company
• Contribute analyses and recommendations to executive forums, and co-author the supply narrative shared with the CTO and staff

Minimum Qualifications

• Strong technical individual-contributor background in data science, analytics, or operations research
• Demonstrated comfort reasoning about resource allocation and trade-offs under constraints — drawn to systems problems, not just dashboards
• Working fluency with causal inference — able to recognize when an effect needs to be identified, not just measured, and to choose an appropriate design
• Deep proficiency with Python, SQL, and data visualization tools
• Track record of owning analyses end-to-end and communicating results clearly to engineering and product leadership
• Direct experience working closely with engineering teams on production systems
• Alignment with Anthropic's mission of building helpful, honest, and harmless AI

Preferred Qualifications

• Significant technical individual-contributor experience in data science, analytics, or operations research at staff level scope
• Experience with highly complex systems with many interacting components (ad networks, payment processing, marketplace matching, routing, etc.)
• Hands-on operations-research depth: experience formulating and shipping real-time constrained-allocation, routing, or scheduling problems in production (LP/MILP, queueing, or RL-based control), with the ability to defend modeling choices
• Causal-inference depth beyond off-the-shelf quasi-experimental templates — particularly methods for recovering long-term impact from short-horizon data: surrogate/proxy-outcome models, off-policy evaluation and counterfactual policy learning, or structural approaches, built rather than merely run
• Experience contributing to or designing experimentation platforms, not just using them
• Exposure to AI/ML products, large language models, or large-scale inference systems
• Track record of setting technical direction across multiple workstreams or mentoring senior individual contributors without formal management responsibility