SHOW NOTES [00:04:30] The fascination with churn [00:08:04] How much more hyped do you think the field has become since you first broke into it? [00:16:01] W

SHOW NOTES [00:04:30] The fascination with churn [00:08:04] How much more hyped do you think the field has become since you first broke into it? [00:16:01] What is Churn? Is that what we do we make butter. [00:17:27] So why is churn so hard to fight? [00:21:48] The importance of metrics in our battle against churn [00:24:01] How do we go from raw event data to metrics? [00:24:45] How do cohorts help us analyze, predict, and understand churn? [00:27:22] What are ratio metrics and why are they so powerful? [00:33:09] Why are outliers so problematic to deal with? model and get information from them, but without them ruining your numbers. [00:34:57] What are some common mistakes that you've seen Data scientists make when it comes to dealing with outliers? [00:39:14] How to be more thoughtful when it comes to feature engineering? [00:42:31] Debunking the common misconception that the choice of algorithm is the most important thing that contributes to model performance. [00:43:56] Your features don’t need to be the most creative [00:45:28] Your job isn’t over once you deploy the model [00:49:05] What are some things that we need to monitor and track - the context of churn - to make sure that our model is doing what it should be, that is performing as we've designed it? [00:50:26] How COVID is messing up everyone’s churn models [00:53:14] Is data science an art or science? [00:55:24] What are some soft skills that Data scientists are missing that are really going to help them take their careers to the next level? [00:56:51] How could a data scientist develop their business acumen and their product sense [00:57:44] What to do with these crazy job descriptions [00:59:27] What’s the one thing you want people to learn from your story? [01:00:39] The lightning round