Data Scientist, Finance Forecasting

Anthropic • San Francisco, CA

5h • Hybrid

About The Position

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.
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 establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization. In this unique company, technology, and moment in history, your work will be critical to ensuring accurate financial reporting, streamlining accounting processes, and supporting our financial operations as we deploy safe, frontier AI at scale to the world.
As a founding Data Scientist on Anthropic's Finance Forecasting team, you'll own revenue forecasting models that drive capacity planning and board reporting, build the backtesting and accuracy discipline that makes those forecasts trustworthy, and lead causal measurement work that tells us how much a given launch or event actually moved revenue — turning what is currently a best-guess conversation into a designed, repeatable readout.
We're hiring for two seats on this team, and we expect candidates to lead with depth in either production time-series forecasting or causal inference. Both shapes are first-class; tell us in your application which one feels more like you.
This is a build role on a small team. The forecast is visible inside the company, and when it's wrong, people notice. We're looking for someone who would rather have their models scored than stay vague, and who is excited to set the bar for how forecasting is done here.

Requirements

• Have substantial experience in data science, forecasting, or quantitative finance, including time owning models in production rather than only in notebooks
• Are deeply fluent in Python and SQL and comfortable productionizing what you build
• Have a strong applied statistics foundation, with depth in either production time-series methods (Prophet, ETS, ARIMA, gradient-boosted approaches, neural forecasting, hierarchical reconciliation) or causal inference (difference-in-differences, synthetic control, Bayesian structural time series, event studies)
• Have built backtesting and accuracy-tracking discipline before and are comfortable having your models scored publicly
• Have presented and defended a forecast or causal estimate to executives
• Have a bias for action and do your best work in ambiguous, early-stage environments
• 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

Nice To Haves

• Experience with exogenous-regressor or event-aware forecasting approaches
• Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems)
• Background in pricing and elasticity modeling or marketing-mix modeling
• Experience forecasting a consumption-based or usage-billed business (cloud, API, marketplace)

Responsibilities

• Own a core piece of the team's modeling work — either the production revenue forecasts themselves (scoping, development, backtesting, deployment, monitoring) or the causal measurement program for launches and events
• Build and run backtesting and accuracy tracking for your models, and use the results to improve quality cycle over cycle
• Contribute to the team's broader research direction, including event-aware forecast architectures, hierarchical reconciliation, and causal designs that hold up under launch-driven step-changes
• Translate model output and accuracy results into clear recommendations for Finance and executive leadership
• Partner with the team's Analytics Engineer on feature pipelines, model deployment, and the forecast store

Benefits

• We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level