Being a data scientist today is like being a webmaster 15 years ago, a sort of jack of all trades in a brand new blooming job domain. IMO the term "data scientist" is not meant to stay forever: as there are no more webmasters today but rather front-end devs, sysadmins, designers, people dealing with SEO, online advertising, etc. There will be dedicataded branches of data science specializations, in data scraping/munging, machine-learning, database management, dataviz...
Another interesting aspect here are "Data Science" programs in colleges/universities. My experience looking at new grads is they are all focused on statistical and computational modeling/algos, albeit with a lot of variability in the depth of their education (e.g. apply a linear model in SAS vs specify a new covariance structure to the errors and derive the respective equations or code a linear model with this).
This has created a situation where lots of people want to get into a specific Data niche and they see Data Science as one thing. But how many Data Modeling jobs are there and how many can there be? Especially when you consider how quickly statistical and machine learning is being automated and is built into software.
Any company working in Data does lots of stuff outside of this. This includes BI, Data Integration, DB Modeling, Custom Visualizations (e.g. D3), Security, Customization, and sitting in front of clients to scope projects and explain how to get value from their Data.
We have a person who is amazing at getting customer authentication integrated into our Data product. So when a customer asks "We use a token and blah blah blah for our data security" this person figures out how to get our software to authenticate through their system. This person is a great R programmer, but they know the bare minimum about modeling in R.
If our statistical model is half-baked that might jeopardize a deal. But If we can't use their security and authentication infrastructure the deal is dead - no compromise there. So this person is really critical in our data team, but they aren't what a lot of people see as critical to Data Science.
The growth of Data jobs is going to be huge if we do not only include the jobs which require R modeling (or related) skills. A lot of people are going to need to let go of "I learned statistics so the only job I want to do is statistical modeling" to get jobs in this industry.
i think this is a very important point - and basically refutes the hypothesis we are in a data scientist bubble.
Right now the term 'data scientist' is far too generic for me and covers so many bases. I graduated university with a strong stats background however it has been difficult for me to transition into solid data science roles due to my lack of programming (which im thankfully improving now) where as i think there is a big risk of some bad data science being done by people who can start messing with data without a solid grounding on what it means for data to be skewed, checking for kurtosis etc etc. For me personally i always knew what i wanted to do with the data, but getting it and cleaning it up was another battle completely. It might be that small discrete data science teams can work together just like any scientific lab would with a technician, professor etc in a bit more of an academic fashion but running under lean methodology
The problems will come from (i anticipate) data scientists who are employed by big corporates because 'we need a data scientist' and HR basically dont understand what they are hiring for or why.
Its more of less on the data science community to self police this - which will likely be the case.
