Data/ML/AITable-stakes
AutoML
Also written as Automated Machine Learning, H2O.ai, DataRobot
Tools that automatically try many model types and settings and pick the best-performing one, so a working model can be built with far less hand-tuning. H2O.ai, DataRobot and the big cloud AI platforms all offer it.
Think of it like
A camera's auto mode: good results quickly, but a professional still knows when to switch to manual.
Junior or senior?
Useful, but it automates exactly the part junior data scientists used to do by hand, so on its own it signals little.
Senior sounds like
Can explain when AutoML's answer wasn't good enough and what they did instead, usually better data or features.
Ask them
“When did an AutoML result not hold up, and what did you do about it?”