Data Scientist, Finance Forecasting

Highlights

• Own revenue forecasts driving capacity planning and board reporting.
• Build trusted backtesting discipline that makes forecasts reliably accurate.
• Measure real revenue impact of launches with repeatable causal readouts.

What they offer

• competitive compensation and benefits
• optional equity donation matching
• generous vacation and parental leave
• flexible working hours
• lovely office space for collaboration

Ideal candidate

A forecasting or causal inference specialist who owns production models end-to-end, thrives on having forecasts scored publicly, and turns ambiguous finance questions into clear executive guidance.

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 or causal inference
• 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