Analytics, Finance & Strategy
AnthropicSan Francisco, CA | New York City, NY | Seattle, WAPosted 11 April 2026
Job Description
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. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
As an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in our company's mission of building safe and beneficial artificial intelligence by delivering the analysis and analytics infrastructure that shape how Finance Strategy (FS) understands Anthropic's business. In this unique company, technology, and moment in history, your work will directly inform how leadership navigates one of the fastest-growing businesses in the world.
This is a role spanning data analysis, business intelligence and analytics engineering, partnering closely across several cross functional teams including Data Engineering, Data Science Analytics, and Finance Strategy. You will produce high-impact, high-visibility analyses that answer leadership's most pressing questions about the financial trajectory of the business—revenue drivers, margin dynamics, compute economics, customer segments, and the forces shaping our PL. You'll also build the datasets, pipelines, and BI foundations that make those insights possible at scale and ensure every number the executive team sees is one they can trust. You've worked in cultures of excellence in the past and are eager to apply that experience to both the craft of analysis and the rigor of building durable data systems as our company goes through a phase of rapid growth.
Responsibilities:
Deliver high-impact analyses and deep dives that surface key insights about Anthropic's financial performance directly to finance and executive leadership
Build and maintain canonical datasets, dashboards, and scaled reporting solutions that provide real-time financial visibility across the company
Partner with Finance Strategy teams (Product, Compute, GTM, and Corporate) to answer their most important questions and streamline recurring analytical workflows
Establish analytics engineering and BI best practices for FS, including data management, governance, and reporting standards
Partner with Data Infrastructure, Analytics Engineering, and Financial Systems teams to develop scalable data pipelines supporting finance and accounting analytics
Champion data-informed decision making across the organization
You may be a good fit if you:
Have 6+ years of hands-on experience spanning data analysis, business intelligence, and/or analytics engineering, ideally with exposure to Finance Strategy, Data Infrastructure and Analytics Engineering
Are highly proficient in SQL and Python for analysis, data manipulation, ETL/ELT, and automation
Have a track record of producing analyses that have meaningfully influenced business or leadership decisions
Have extensive experience with data visualization and using Claude / AI tools for data analysis and BI
Have experience with cloud platforms (AWS, GCP) and modern data stack tools (dbt, Airflow, etc.)
Have hands-on experience working with financial systems and building downstream data models from these systems
Excel at translating complex business questions into both sharp analyses and durable technical solutions, and can communicate effectively with technical and non-technical stakeholders, including executives
Are detail-oriented with a deep commitment to data accuracy
Thrive in fast-paced environments, can manage multiple priorities while maintaining high-quality deliverables
Operate with an ownership mindset, taking projects end-to-end from ambiguous question to trusted answer and seeing them through to impact
Strong candidates may have:
Championed AI driven analytics and financial analysis within their organizations
Prior experience in high-growth technology companies or start ... (truncated, view full listing at source)
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