Data Scientist, Finance Forecasting

Anthropic•San Francisco, CA

10h•Hybrid

About The Position

As a founding Data Scientist on Anthropic’s Finance Forecasting team, you will own revenue forecasting models that support capacity planning and board reporting, establish backtesting and accuracy practices, and lead causal measurement work to assess how launches and events affect revenue. The team is hiring for two positions and welcomes candidates whose strengths are primarily in either production time-series forecasting or causal inference. This is a build role on a small team. You will help establish forecasting standards and develop models whose performance is visible across the company. The work includes modeling, measurement, communication of results, and collaboration with the Analytics Engineer on feature pipelines, deployment, and the forecast store. Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The Finance Analytics and Business Intelligence team builds analytics engineering and business intelligence capabilities for the Finance & Accounting organization, supporting accurate financial reporting, more efficient accounting processes, and financial operations as the company deploys AI systems at scale.

Requirements

Substantial experience in data science, forecasting, or quantitative finance, including experience owning models in production rather than working only in notebooks.
Deep fluency in Python and SQL, and comfort productionizing work.
Strong applied statistics foundation and depth in either production time-series methods (such as Prophet, ETS, ARIMA, gradient-boosted approaches, neural forecasting, or hierarchical reconciliation) or causal inference (such as difference-in-differences, synthetic control, Bayesian structural time series, or event studies).
Previous 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 do effective work in ambiguous, early-stage environments.
Bachelor’s degree or an equivalent combination of education, training, and/or experience.
A field of study relevant to the role, 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 businesses.

Responsibilities

Own a core part 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 on event-aware forecast architectures, hierarchical reconciliation, and causal designs robust to launch-driven step-changes.
Translate model outputs 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.