Careers and AI · Data scientist / ML
The verdict for a data scientist / ml
Boilerplate generates itself; framing the problem, validating, and judging what's real remain.
What it means that it mutates: powerful chunks get automated, but the judgment, the relationship, the taste and the responsibility stay human. The trade reorganises; it isn't erased.
In O*NET’s RIASEC framework, data scientist / ml maps to a IRC profile within the Technology family. Which is to say, it turns mostly on:
I · Investigative: understanding, analysing, solving problems.
R · Realistic: doing, building, the physical and the tangible.
C · Conventional: ordering, organising, the systematic.
Your trade doesn't disappear, it reorganises: what gets automated stops being paid for, and the value moves up a level. Whoever directs the AI wins; whoever competes with it, loses.
The play for a data scientist / ml:
The model is a commodity; framing the problem is not. Your moat is knowing what is worth modelling, which data can be trusted, and how much a mistake costs.
They share a family or interests with data scientist / ml. Useful if you’re thinking about a move.
Software developer
Mutates
Data analyst
Mutates
Systems administrator
Mutates
Cybersecurity engineer
Holds up
QA / software tester
Erodes
UX designer
Mutates
And you? Does this profession actually fit you?
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