Financial services · 2025
Series B fintech
Data ScienceForecastingProduct
Forecasting platform that the CFO actually trusts
- Challenge
- Revenue forecasts were rebuilt by hand each month in a sprawling spreadsheet. Finance and the data team disagreed on the numbers.
- Approach
- Designed a forecasting service with reproducible models, scenario inputs and a thin review UI. Wrote evals against past months so accuracy is measured, not asserted.
- Outcome
- Monthly close cut from 6 days to 2. Forecast error down ~38%. One source of truth shared by Finance, RevOps and the board deck.