Data Scientist, Finance Forecasting

Anthropic•San Francisco, CA

2h•$270,000 - $345,000•Hybrid

About The Position

As an early member of Anthropic’s Finance Analytics and Business Intelligence team, the Data Scientist will establish analytics engineering and business intelligence capabilities for the Finance & Accounting organization. The role supports accurate financial reporting, streamlined accounting processes, and financial operations as Anthropic deploys AI systems at scale. As a founding member of the Finance Forecasting team, the Data Scientist will own revenue forecasting models used for capacity planning and board reporting, build backtesting and accuracy practices, and lead causal measurement to assess how launches and events affect revenue. The team is hiring for two positions, with candidates expected to bring particular depth in either production time-series forecasting or causal inference. This is a build role on a small team, with visible forecasts and responsibility for setting standards for how forecasting is done.

Requirements

Substantial experience in data science, forecasting, or quantitative finance, including experience owning models in production rather than only in notebooks.
Deep fluency in Python and SQL and comfort productionizing work.
Strong applied statistics foundation, with depth in either production time-series methods or causal inference.
Experience with production time-series methods such as Prophet, ETS, ARIMA, gradient-boosted approaches, neural forecasting, or hierarchical reconciliation; or causal inference methods such as difference-in-differences, synthetic control, Bayesian structural time series, or event studies.
Experience building backtesting and accuracy-tracking practices and comfort with models being scored publicly.
Experience presenting and defending a forecast or causal estimate to executives.
Bias for action and ability to work effectively in ambiguous, early-stage environments.
Bachelor’s degree or an equivalent combination of education, training, and/or experience.
Education or experience in a field relevant to the role, as demonstrated through coursework, training, or professional experience.

Nice To Haves

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

Responsibilities

Own a core area of the team’s modeling work: production revenue forecasts, including scoping, development, backtesting, deployment, and monitoring, or the causal measurement program for launches and events.
Build and run backtesting and accuracy tracking for models, using results to improve quality over time.
Contribute to research directions including event-aware forecast architectures, hierarchical reconciliation, and causal designs that address 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.