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Nick (Domino Data Lab)

Co-Founder, President & CPO, Domino Data Lab · 1 appearance on the record.

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founderexecutiveLinkedIn ↗domino.ai ↗

Nick Elprin co-founded Domino Data Lab in 2013 to provide an enterprise AI and MLOps platform that helps Fortune 100 companies build, govern, and deploy data science models at scale. Prior to Domino, he built quantitative analysis tools and research platforms at Bridgewater Associates.

9statements → 2claims → 0claims resolved → 3.67/5average certainty → 2/5average debate potential →

2 not checkable as stated how the 2 claims stand · each chip opens the sources

2 assertions · 1 opinion · 6 insights · every statement was checked. The predictions and assertions are the 2 claims: statements the public record can support or contradict. 0 are resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Nick argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

266 words/min while actually speaking · 30.2 um and uh per 1k words

No argument clarity score for Nick (Domino Data Lab): only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,703 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Nick (Domino Data Lab) said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion
Elprin: Apache Spark still generates more industry hype than actual business value
“I think there's still more hype around Spark than actual value extraction from it.”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 15:56 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: Elite data science organizations prioritize collective knowledge over solo practitioners
“The best organizations we've seen think of their work as contributing to collective knowledge.”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 2:23 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: Data science progress relies on compounding small insights, not epiphanies
“Progress actually comes from lots of little insights that compound over time”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 2:49 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: Agility to experiment with new tools beats single-platform lock-in
“That's never the answer. It's much more about having agility to let people rapidly experiment with whatever the next thing is that comes out”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 3:45 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: True data science reproducibility requires code, data, results, and environment
“What we've learned is that for this kind of work, this advanced analytics quantitative research work, reproducibility is more than just, hey, we have a snapshot of the code. You know, like, that's, you know, it's great for software engineering. GitHub does tha…”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 6:11 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Assertion Not checkable as stated
Elprin: Regulatory compliance drives enterprise demand for data science reproducibility tools
“One of the really interesting things about reproducibility we've learned is that it's been extremely resonant in industries where there are regulatory and compliance concerns, and we're sort of seeing that more and more, especially in financial services, they'…”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 7:03 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: Drive organizational best practices by packaging them inside individual productivity tools
“What those people seem to want is the ability to test more ideas faster, that experimental agility some ways to sort of expose their work more out into the business. But let's package that in a way that automates or incentivizes best practices.”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 8:51 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Assertion Not checkable as stated
Elprin: Highly sophisticated enterprise data teams often operate outside strict compliance walls
“I think that the folks who are doing sufficient, sufficiently sophisticated work to really want to use what we're doing tend to be in groups where they don't have those restrictions.”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 19:23 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
Insight
Elprin: Rapid model deployment creates feedback loops that sustain team funding
“If you've actually built something that works, how quickly can you get it out into the business? Because that, that creates the feedback loop that that creates credibility and buy-in to continue sort of investing in the work that's going on.”
Nick (Domino Data Lab) Nov 9, 2016 ▶ 7:52 Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]

Appearances (1)

EpisodeDateSpeaking time
Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Drive Nov 9, 2016 17m
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