Data Scientist, Supply

Details

About Anthropic

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. The team is a group of researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

Anthropic is compute-constrained, and how the company allocates compute is one of the highest-leverage decisions. This role addresses two intertwined problems at the heart of how compute is allocated.

1) Allocation problem

Matching a volatile, heterogeneous stream of demand to a finite, heterogeneous fleet of chips. The team navigates questions such as:

• Which models run on which hardware
• Which regions
• Under what serving configurations
• With demand shifting and capacity bounded

The role focuses on building metrics and analytical frameworks that make the trade-offs legible, and partnering with infrastructure teams that own these systems to turn understanding into better decisions.

2) Causal-inference problem

There are many levers—rate limits, pricing, cache behavior, capacity shifts, and routing changes—but only a partial picture of what pulling each lever does to users. The role builds causal understanding so that allocation decisions are made on expected user impact rather than intuition.

The role is a fit for someone who:

• Thinks natively in terms of constrained allocation and queueing
• Treats “what would happen if we changed X” as an identification problem rather than a dashboard query
• Wants their work to translate into operational and productionized change

Findings are presented to senior leadership.

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

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

Preferred qualifications

• 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)
• 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

Minimum education and experience

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Logistics

• Location-based hybrid policy: Currently, Anthropic expects all staff to be in one of its offices at least 25% of the time (some roles may require more time in offices)
• Visa sponsorship: We do sponsor visas. However, Anthropic isn't able to sponsor visas for every role and every candidate. If an offer is made, they will make every reasonable effort to get you a visa and retain an immigration lawyer to help with this.