The job in which you are magically given a pristine dataset and can just tweak ML models all day doesn’t exist.
It's a bit of both really. If the data science job is in the field of predictive maintenance for example, then a (relatively) simple model may be sufficient to add business value straight away, and the hard part is a deep understanding of the potential failure modes of the machinery you're predicting on and the kinds of sensors used to gather the raw data. There's no "one size fits all".
This is one of my favourite papers on the subject, written by one of the lecturers on the DS course I did: https://users.cs.duke.edu/~cynthia/docs/RudinETAL2010.pdf
Whoever is running data scientist units needs to realise you need IT/DBA/programming people in the mix. Statisticians in my experience cannot design databases, concepts of code reuse and normalisation do not exist in their vocabulary. A great deal of time is wasted doing repetitive tasks that someone with a programming background would have assisted.
That's the actual job! The theoretical investigation. Not the programming. The programming is really easy. which is part of why having 15 years in software doesn't have so much weight.