Data Scientist, Finance Forecasting

Join Anthropic as a Data Scientist in Finance Forecasting, where you'll develop revenue forecasting models and enhance financial analytics for AI systems. Competitive salary and hybrid work environment.

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.

• •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, including establishing a cross-methodology accuracy baseline as a standing Finance metric, 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.

• •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).

• •Annual Salary: $265,000 — $320,000 USD.

• •Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
• •Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• •Visa sponsorship: We do sponsor visas!

• •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.