I work at a firm with many data scientists (I am one of them -- though my title has wavered back and forth between data scientist and ML engineer). Whether or not data scientists are "technical" and in what sense could be a difficult question to answer.
I can't speak very broadly, but at least for my company most data scientists are not doing the kind of work you describe. There certainly are some folks constructing and training complex machine learning models, but I think the majority work on the level of more basic statistical models and rules of thumb, where a project's final output might be a dashboard or presentation. Arguably some might refer to this as data analysis rather than data science, but none of these terms are particularly well defined.
That's not to say they aren't technical in some sense. All of them can and do code to one degree or another (with perhaps the exception of a small number of people who've been in the industry for decades), though not all of them do so with high proficiency or attention to software engineering best practices. That also goes for some of the engineers where I work, admittedly.
All in all, the broad level of technical aptitude has grown over the past few years. But not everyone with the title of data scientist is a machine learning specialist, nor are they necessarily skilled at software engineering.
Edit: As for Copilot, I found it worse than useless. It miserably failed every test I threw at it, from machine learning to (especially) Spark data pipelines, only redeeming itself with a string handling problem -- for which the solution was still entirely wrong but at least interestingly wrong. I frankly don't see how anyone pays for it, though perhaps it's better for projects with a ton of boilerplate.