Data Science Isn't Done in a Bubble

When you're scoping out a project, data science isn't done in a bubble. We need to understand what are the rements? You know, if i'm working with an advertising team, what are the metrics that they are using? What metrics drive their decisions? And how can i make that metric be how can i set up a model that captures and produces that metric? It's not about getting point o one % improvements and accuracy. Sometimes when you have huge variability, for example, you now just understandin where are the levers that are important to really push ons.