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 →

Joseph Essas 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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1exchanges match
1on raw tape
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Answered raw tape D 5 · C 5 · P 5 · Cm 4 4.85

Q Great, thank you so much. Um, I'll, I'll pass on to my two people here, but, uh, what does a data science team look like, or who, who does all of this?

A Um, so, a data science team, in our case, it's, um, Uh, six data scientists, um, as well as, uh, several data science, uh, what we call data science engineers, and so on the engineering side, uh, those would be folks who would be moving data around and creating all this infrastructure, and data science would be the guys who are actually, uh, modeling for things, uh, developing personalization engines, uh, trying to find interesting, uh, tidbits of information inside data, Um, and work, ultimately working with the business. So one thing which is, um, we try to be very, um, very hard about is that whatever we do, it ultimately needs to be solving some kind of a problem in a business. Problem in a business doesn't mean necessarily for OpenTable. Problem for our customers, for diners, for restaurants. Um, so we look at a more pragmatic approach to data science, and, and, um, that's on one side. Lastly, data science team would be working on things like inventory optimization inside restaurants. So that's also, I didn't touch on that area at all, but It's a big area of how the tables and slots of tables should be allocated. You know, if a restaurant opened from six to nine and somebody takes a seven p.m. reservation, they essentially block that restaurant for the whole night, rather than somebody would take a six o'clock or eight o'clock, and then a restaurant can fit two parties tha…

AI assessment note: “six data scientists, um, as well as, uh, several data science”

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