The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

John Rauser no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q you very much. That was really, really, uh, interesting and fun. Um, just to drive the point home from a, a practical perspective, um, uh, as somebody at Pinterest who presumably, um, hires data scientists, um, how do you think about that? If, You know, software engineers, uh, yeah, software versus, uh, statistics in hiring decisions. I mean, I, I guess the, the talk sort of answer the question, but.

A Yeah, I mean, I think, ah, like a working data scientist has to be able to code, because if they can't, they can't fish for themselves, right? They have to go and, and wait, as, as it was previously said, ah, for someone to go hand them their beautiful data set on a silver platter, right? So if you can code, you can go off and, and grovel around in, in databases, or you can write logging code to get the data that you need. Um, like you have to be able to code, um, but you also have to have the analytical chops, right? And, like, exactly where the balance is between the two is, um, there's a wide range of possibilities. Um, I gave a whole talk about that at Strata two years ago or something like that, which you can find on YouTube, uh, if you're interested.

AI assessment note: “a working data scientist has to be able to code, because if they can't, they can't fish”

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