I've got a PhD in statistics and consider myself a pretty good data scientist, but I am not a computer scientist. The last job I applied for asked me to perform a coding challenge prior to a possible in-person interview. The challenge was heavily focused on algorithms and data structures, almost nothing about statistical inference, causal inference, experiment design, model fitting, etc. I didn't bother submitting my answers.
I've got a feeling that these large corporations have started to realise that the 'data scientists' (commerce and engineering grads that know some python) they are hiring are all pretty useless because they don't have the engineering skills to actually get anything done.
A lot of the job advertisments for 'data scientists' I see these days focus WAY more on things like 'AWS, Spark, building pipelines, etc' than statistics and modelling skills.
Slightly related note, I'm seeing a silly number of advertisments asking for PhD's in quantitative fields and experience with PowerBI. That'd get me running for the hills.
There's work to do around data (databases, logs, analytics, marketing, business intelligence, etc...) but none of it has to do with stats/maths. The realization is quite hurtful for those who wished to pursue data science.
The only field that recruits maths folks is finance, specifically quants, who are doing financial analysis all day. It's only a few roles in a few places and highly selective.
People are impatient to do math, they'd rather want to try 100 things at once, see what works. So I am not surprised they don't care in the interviews.
Only when someone writes a fancy engineering blog post or paper, weird equations appear out of nowhere..
This is so much visible in deep learning papers. The equations are there just to impress.