Exactly this. Implementing algorithms that work on well-defined clean data isn't difficult. Working with naive problems where the right algorithm isn't clear or

Exactly this. Implementing algorithms that work on well-defined clean data isn't difficult. Working with naive problems where the right algorithm isn't clear or can't be directly implemented is difficult. In many cases cost of implementation is significant. You can't just apply a model to millions of points of data and blindly expect to get anywhere without fundamentally understanding the data you're working with first. That's what separates data scientists from data engineers, and the ability to take a naive problem and work with it to a conclusion in a manner that's understandable both from a technical and business point of view is exactly why having a PhD is typically a barrier of entry because that's all academics in data science do all day!