> Good DS starts simple, ships, and then iterates. Bad DS starts with the most advanced technique they know. > Good DS is constantly learning & evolving their

> Good DS starts simple, ships, and then iterates. Bad DS starts with the most advanced technique they know. > Good DS is constantly learning & evolving their toolbox. Bad DS stagnates and sticks with what they know. These are the big ones imo. But not super obvious. As a junior data scientist I never needed to use anything but regularized linear models and decision trees. Maybe a random forest but the explainability usually wasn't worth it. Recent explainability tools like SHAP have changed this somewhat. But for the most part I think its still ok for the average data scientist to be regularized linear models, decision trees, and then occasionally, idk, a LightGBM or Catboost + SHAP for explainability. A lot of people still don't know about these, and it's now a decent test for whether people are really trying to stay up to date. But if they're not, I don't really care.