Is “data scientist” the new “programmer”?

I agree. I was in a very prestige organization and they didn't know what a statistician really do and just hired CS machine learning PHDs. Even those people don't even know what statistician does. One person gave me ISLR when I ask for advice to get hire at this prestige place (I did an equivalent of this over several graduate courses in statistic program).

Another person proudly told me that in his project, he was using GLM stating he knows GLM. I asked what was the link function and that person stated he didn't know and it's somewhere in the code...

I've since then double down on statistic and will be going into Biostatistic field instead of data science. It feels like there are a lot of impostors especially in start ups and government organizations in data science. I have no clue why but there is just this culture in tech industry that have made me left it for better field. I've intern in the biostat field it is much better. CNN and other have listed with high quality of life.

I think this is one of the worst possible attitudes to have. It’s a petty way to feel, desiring some type of “I’ve seen some shit” tough guy credential more than supporting the stuff needed to actually solve business problems.

If you hire people whose value add to your company is inventing completely new things, including deployment, ops, scaling, etc., that goes along with that, then it is the job of infrastructure on the other side of that fence to happily and eagerly accept whatever is tossed over the fence, to understand why developer teams made the choices they made, and to take an attitude of supporting as much as possible.

> “You need a plan for how all the components will be maintained, patched, and upgraded. You need contingency planning when some of those bleeding edge components are going to become deprecated. You need to consider what staff capabilities are assigned to do that maintenance work or what will happen when the institutional knowledge used to form the original integration is not on-call to reintegrate it in the face of unexpected events.”

Yes, of course. But all this is already what dev teams are doing. Ops / infra is not taking a hare-brained plan and adding these robustness aspects into it. Not at all. Instead they take plans from application teams and try to use policy to minimize their own maintenance burden, even when that optimization is antithetical to what the company requires at a more fundamental level.

A lot of companies languish and die because of sociological dysfunction in the policy interface between dev teams and infrastructure. The more that infrastructure has political control of that interface, the closer to death is that company.

It’s like a body that is disallowed from generating white blood cells in response to a new immune challenge. Even if the bleeding edge integrations are really hard, the alternative world where you slow them down with policy is death and attrition.