Safeguarding Your Career in the World of Automation
- by 7wData
"Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass Automation on the horizon for numerous professions, what can we do to safeguard our positions?
Recently, popular career site Glassdoor published its updated list of Best Jobs in America, with data scientist taking top spot for the year. Glassdoor gave the career a job score of 4.8 out of 5, a $110,000 median base salary, and noted 4,000 job openings at the time of writing.
The announcement was rather unsurprising, given the fact that it was a repeat of the previous years' findings:
Just a few months ago, job search site CareerCast also published its list of 10 Best IT & Engineering Jobs 2016, which -- surprise! -- also noted data scientist as the top position of the year, with an annual median salary of $128,240, and a growth outlook rate of 16%.
This is all great news.
However, other recent headlines such as "80% of IT Jobs can be Replaced by Automation, and it’s 'Exciting'" and "AI learns to write its own code by stealing from other programs" seem to be, decidedly, less great, especially for those of us in industries projected to be affected by artificial intelligence and its oncoming wave of automation (FYI - "IT" jobs are not the only ones in trouble).
data science & Engineering recruitment specialist -- and author of a great series of articles on becoming a data scientist -- Alec Smith, offers this take on automation in the realm of data science:
Given this disruptive age we are living in, automation is something I have given a lot of thought to; not just in terms of how it might affect the data scientists that I place, but also the potential impact on my humble profession. And while data science and recruitment are not comparable disciplines, I see a lot of parallels when it comes to how automation might affect the practitioners within both fields. Although I think that recruitment is primed for a Machine Learning led evolution, it – like data science – is a multi-faceted process, and deeply rooted in a human system, thus making it a difficult problem to fully automate.
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