Data Scientist, Finance Forecasting na Anthropic

Híbrido - San Francisco, CA

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As an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in building safe and beneficial artificial intelligence by establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization. You will own revenue forecasting models that drive capacity planning and board reporting, build backtesting and accuracy discipline, and lead causal measurement work that quantifies the impact of launches and events. This is a build role on a small team that requires depth in production time-series forecasting or causal inference.

Salary

USD 265,000 - 320,000

Requirements

Skills

  • Substantial experience in data science, forecasting, or quantitative finance, including time owning models in production rather than only in notebooks
  • Deeply fluent in Python and SQL and comfortable productionizing what you build
  • 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)
  • Built backtesting and accuracy-tracking discipline before and comfortable having your models scored publicly
  • Presented and defended a forecast or causal estimate to executives
  • Bias for action and do your best work in ambiguous, early-stage environments

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, 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

Technologies

PythonSQLProphetETSARIMAGradient-boosted approachesNeural forecastingHierarchical reconciliationDifference-in-differencesSynthetic controlBayesian structural time seriesEvent studiesEvent-aware forecastingHybrid or foundation-model forecasting (TimeGPT-class systems)Pricing and elasticity modelingMarketing-mix modeling

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