Imposter Syndrome (in Data Science) (opens in new tab)

We will distinguish 'machine learning specialist data scientist' from 'database specialist data scientist' just like we distinguish 'electrical engineer' from 'lab systems engineer', for example.

Then, we might have a term for generalists like 'data science technician'. And by then, the people who 'aren't really good at any aspect of it' and can't really function as generalists will be naturally sorted out because they can't really fit into any of those titles

You can dress up bad data in any number of ways to get results that sound and look pretty. I see this all the time. Sometimes you get lucky and the model is ok regardless. Lots of times the model performance isn't great, and it is later assumed there are other outside issues to blame, or the project is redone for the umpteenth time. Ocassionally you will have colossal failures that do real damage.

Keep in mind that when a poorly designed machine fails and kills dozens, or the financial system of the world crumbles under the weight of terrible loans and convoluted financial instruments, or millions of people's personal info gets hacked due to terrible, antiquated security systems, and everyone starts asking "How could people be so stupid to let something like this happen?", the answer is almost always executives, management, or "humble engineers" sweeping the problems they don't like under the rug because they believe "it's 10x better to just work with whatever you have available and get shit done".

So for instance, I totally feel like a data science imposter, but in the last year have done the following: - Pushed a custom deep learning NLP model to production - Created and maintain company's ETL and data warehouse mechanisms - Performed statistical analyses to find ways to better target and increase customer engagement. - Implemented event tracking and performance metrics across products - A sales prediction product that has contributed to $~5M in incremental revenue

Somebody obviously believes in me since I've grown the team from just myself to ~6, but I also know that I've had dozens of past colleagues that would instantly disqualify me since I 1) don't have a PhD and/or 2) can't/don't read statistics/machine learning papers

Combine with the value that good ones can provide, and you have a perfect situation where a boss or peer just thinks thar be dragons in the work sphere of the statistician.