We had to admit, we were a little curious at Bitvore when we saw the title of this article from House of Bots, by Kimberly Cook: "Why So Many Data Scientists are Leaving Their Jobs, 4 Most Important Reasons." Being a team of data scientists, this article got our attention.
The article contends that data scientists typically "spend 1-2 hours a week looking for a new job." Not us of course. But we wondered ... why?
1. Expectation does not match reality.
Well duh. When does it ever? We were particularly intrigued by this quote "A large number of companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. On top of that, many of these companies fail to hire senior/experienced data practitioners before hiring juniors." We have seen this time and time again. Without the correct infrastructure, data scientists end up simply putting analytics in place vs. cranking out the insights their companies' need to crush the competition.
2. Politics reign supreme.Literally no comment. Data Scientists are not good at BS and don't appreciate it, nor like it. Well, okay. That was a comment.
3. You're the go to person about anything data.
In the beginning, being king of the data at a start-up can be pretty fun. And, if you come to data science from a software engineering and devops background, you can, literally, do everything. But after awhile it seems as if your colleagues think you are "Data and Reporting Dude" rather than "Insights Engineer." Most companies don't know how to utilize a true data scientist. Expectations vs. reality can, well, bite.
4. Working in an isolated team.
Being the data Maytag repair guy (okay that was an OLD analogy, I apologize), sometimes really is no fun. Working in isolation and struggling to provide value, simply is not a good time. And yes, sometimes product strategy and data science don't work on the same timescale. Or the same time space continuum for that matter.
We recommend, when looking for a new position, data scientists need to check closely for alignment of goals between why the hiring organization needs a data scientist and the organization's expectations of value contribution from their future data scientist versus what the data scientist can actually provide. For example, at Bitvore, understanding insights that underpin the data is everything, and Bitvore is a GREAT company for an aspiring data scientist.
On the positive side of all this, these are typical frustrations that come with being the vanguard of any new profession ... remember the rooms of women mathematicians who were the human computers for NASA from Hidden Figures? The struggle is real.
