Data Scientist, Supply

Anthropic•San Francisco, CA

11d•$285,000 - $460,000•Hybrid

About The Position

Anthropic is seeking a Data Scientist, Supply to address critical compute allocation challenges. This role focuses on matching demand to a finite fleet of chips, bringing structure and analytical rigor to allocation decisions. The position involves building metrics and analytical frameworks to make trade-offs legible and partnering with infrastructure teams to improve decisions. Additionally, the role will tackle causal inference problems to understand the impact of various levers (rate limits, pricing, cache behavior, etc.) on user outcomes. The ideal candidate will have a strong understanding of constrained allocation and queueing systems, and a desire to translate analytical findings into operational changes. This role requires close collaboration with infrastructure engineers and presentation of findings to senior leadership.

Requirements

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.

Nice To Haves

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 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.