Anthropic, an AI safety and research company headquartered in San Francisco, is seeking a Data Scientist, Supply to solve compute allocation challenges. The role focuses on developing metrics, causal inference models, and dashboards that link compute decisions to user outcomes, collaborating closely with infrastructure, product, and research teams to implement operational changes and present findings to senior leadership.
Data Scientist, Supply at Anthropic
On-site - San Francisco, CA | New York City, NY
More jobs at AnthropicSalary
USD 285,000 - 460,000
Requirements
Skills
- 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
- 8+ years of hands-on data science experience
- 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
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
Technologies
PythonSQLdata visualization toolsLP/MILPqueueingRL-based controlsurrogate/proxy-outcome modelsoff-policy evaluationcounterfactual policy learningstructural approacheslarge language modelsinference systems
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