[CinchWrench]

>the fundamental skills that you need are mathematics and software engineering

So much this. If I have to interview another junior-level DS who has a MNIST project in their github and still somehow can't manage fizzbuzz or a fibonacci function I'm probably going to take up religious asceticism.

EDIT: I said junior, but I meant Senior. We're talking people with PhD's who claim to have done extensive software engineering in previous roles.

[kordlessagain]

Try filtering them on their specific ability to visualize things. My neighbor was 80+ year old math guy who could see graphs of equations in his head.

My hypothesis is that good stats people probably don't visualize (Aphant) or visualize very specific types of data in a unique way. Without visualization, people tend to fall back to logical thinking - or emotional thinking, depending.

For example, I have a friend who can look at 2D seismic data and see what the underground formation looks like in his mind, in 3D.

[whoisburbansky]

The person you are replying to seemed to be complaining about their lack of programming ability, not statistical ability, so how would this help in their situation?

[CinchWrench]

Yup, I think I and that child are talking past each other. :-(

It's good though because they raise some really valid points about the importance of intuition. I joke with my colleagues that all we're doing is encoding data as a hilbert space and slapping an algorithm on it, but that elides the fact that intuition like the child is talking about is important for knowing how to build that hilbert space.

[dumb1224]

I think there is an issue with focus of the role. If job is more focused on programming then the candidate must be at least proficient in coding. Otherwise if you need a mathematical modelling person then you need to look into relevant training background (undergrad degree etc). A lot of my colleagues in my research institute are from a physics background. Because molecular biology require a lot of statistics. They are ok coders but what they really contribute is the modelling part.

[6gvONxR4sf7o]

If I have to interview another whose only ML tools are GLMs, random forests and boosted trees (only ever with one hot encoding, of course) I’m going to do the same.

[CinchWrench]

Those and SVR's get me through 99% of the algorithmic part of my job, though!

The rest is some unsupervised stuff like k-means and PCA.

What would you like to see instead? (INB4 CNNs/RNNs other deep learning topics)