Forecasting workflows for launch and capacity planning

Forecasting becomes useful when it changes a conversation, not when it produces a perfect line. The work I enjoy most is turning forecasting into a team habit: something that helps people ask better questions, notice missing assumptions, and reason about uncertainty without becoming paralyzed by it.

That usually looks like simplifying the model enough that others will trust and revisit it, then creating a review cadence where the forecast is not a report card but a decision aid. I prefer range-based thinking, explicit uncertainty, and short loops between a forecast and the actions it is meant to inform.

This is where my interest in data science and operations overlap most directly. The technical work matters, but so does the surrounding workflow that makes the model usable.