AI for Engineers: How (Not) To Be a Data Scientist

February 28th, 2019

In the process of doing this, some of us conclude that software development is the past and data science is the future. That we need to be data scientists to embrace that future or be stuck in the past with (*gasp*) the COBOL developers.

The problem with a developer saying, “I need to be a data scientist”—aside from it being simply wrong—is that becoming a data scientist takes a lot of work. Years worth of it. And after those years, you’re a brand new data scientist.

A solid example of this is the Test Automation Engineer. Writing all that Ruby code with the cucumber and the gherkin, the Test Automation Engineer has added some test automation tools and testing skills to his repertoire but is still a developer. He works with Quality Assurance to help make sure the application is as bug-free as possible. And, since QA folks usually aren’t developers, he can help in ways that other QA persons can’t by bringing development expertise to the rest of the team.


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