Data Scientists
topic on 13 shows · 49 statements across 44 episodes
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49 statements about Data Scientists, every show
Verrilli: Hex and AI match two weeks of 2017 Amazon L7 work
“You know, you can pull data now on a hex thread. That is basically what it would take a week, two weeks with an Amazon L seven, you know, a data scientist in 2017.”
Core disciplines retain distinct advantages in problem framing and architecture
“Data scientists are still going to be experts at Can we trust this data? Are we interpreting it the right way? What's the data versus judgment that we should be applying here? A product manager is still going to be exceptional at saying, have we really framed …”
Mosseri: Companies will regret only hiring senior talent without developing juniors
“Like you can't just have a bunch of super senior data scientists and like no new ones, cuz then who's gonna be the new super senior data scientists in the future. So you need to, Basically hire and mentor and grow talent. You know, maybe the team is smaller ov…”
Sanyal: Most enterprise data scientists are essentially data curators
“The main thing they actually work on, though, is curating data, because at the end of the day, you're not really a scientist sitting building new model architectures. Most data scientists are essentially data curators.”
Bilgen: Chemistry experts rarely possess data science and bioinformatics skills
“People who understand chemistry Tend to be not the kind of data scientists and bioinformaticians that analyze the data.”
Venkatachalam: Unmet global demand for data scientists reaches 200 million
“And so there's only three million data scientists in the world, but the global demand for data science capabilities, if they were readily accessible is probably a couple hundred million, right?”
Catanzaro: Companies Need Moderately Sized Data Teams, Not Armies of Engineers
“And I've become actually convinced that like, well, every company does need analytics engineers and does need data scientists. They probably don't need armies of them. And probably having like a moderately sized data and analytics team is a good thing.”
Chen: Early Startups Should Not Hire Data Scientists as First Employees
“Like I would never hire data scientists when the first three people in a company. And I say that because I used to be a data scientist. Like data scientists are great when you want to optimize your product by two percent or five percent, but that's definitely …”
Ayrey: Data scientists leak API keys more frequently than SREs
“Data scientists leak out API keys and passwords more often than site reliability engineers.”
Palazzi: Embedding data scientists with utility line workers accelerates AI adoption
“So something specific I've seen work very well is to embed teams of data scientists and software engineers with the lines of business. So to make sure that somebody who is a data scientist is actually sitting with a trader for a week, or sitting with the grid …”
Antin: A/B tests rarely explain why user behavior changed
“AB tests are great, but one of my most painful things to do is to sit in a room full of PMs and data scientists who have just seen the results of an experiment that like flipped to Statsig. And then they're like, cool. I was significantly down over this course…”
Taylor: Model observability is the most important focus for data scientists
“I harp on observability a lot because I think it's, like, probably the most important thing a data scientist can focus on”
Tom Pierce: Data Lakes Shift Data Filtering Burdens to Downstream Consumers
“The task of filtering the water to make it drinkable has been shifted away from the data collector, because he just put it in the data lake, drink at your own risk, and when you come, you know, dip your bucket into the well of the data lake, you need to make s…”
Sankar: Palantir targets domain experts, not data scientists, with its AI tools
“I think there's a fair amount of companies I see going after kind of let's call it the canonical data scientists as an archetype of like, I want to fine tune a model and I'm going to go do that. I see a smaller number trying to go after devs as an archetype, b…”
Miller: Dedicated product data scientists identify patterns better than ticket-based analysts
“The other is just that data scientists, as with most humans, like we get better, the more focused we are and the more in depth we are in understanding the product itself. Right. So If you have someone that's dedicated to a zone or an area of the product, then …”
Data scientists do the 'sexiest work' on data teams
“And lastly, you know, your data scientists who, in my opinion, are kind of doing the sexiest work when making business predictions based off of data, their needs are also different.”
Chamath: The title 'data scientist' originated from his team at Facebook
“As classically used in Silicon Valley, it came from Facebook and it came from my team in a critical moment.”
Hyman: Companies without data scientists on LinkedIn are not doing real AI
“And one quick check that I suggest to businesses is go out on LinkedIn and see whether there are any data scientists. If they're not, then there's not really AI going on.”
Eifrem: Data scientists now match developers as Neo4j's primary user persona
“Today, and this happened just in the last 12 to 18 to maybe 20, at most 24 months, data scientists are an equally like as big of a persona for us as the developer. So if you look at kind of our top line metrics around kind of awareness or Visits to neo-for-day…”
Douetteau: Global supply of data scientists cannot meet demand for data problems
“There are not enough data scientists in the world to solve all the data problems we have.”
Granade: Alternative data edge comes from team collaboration, not raw data sources
“And I actually tend to think it has very little to do with the data sources itself or the information. It's the competitive advantage you're looking for is the ability to integrate across your investors with your data scientist, with the people who are sourcin…”
Pesenti: Facebook has as many data scientists as product managers
“So actually, you know, to get an idea, there are as many data scientists in Facebook as they are product managers, right?”
Fontana: 2019 Data Science Tooling Resembles 1980s Software Engineering
“The state of tooling for a data scientist in, so what, 2019 is what it was for a software engineer in like the late eighties, early nineties, There's still so much that's so hard about building these things that could be made a lot easier with the right tools”
Anaconda survey: Under half of users have 'data scientist' in job title
“Out of the, whatever, thousands of thousands of respondents of people who use the Anaconda, less than half actually had data scientists in their title, and most people actually were learning data science to use it in their current job function.”
Hansen: Data scientists usually spend half their time annotating data
“Data scientists usually spend half of this day annotating data instead of writing the clever algorithms that, that, that, what they, that is what they really want to do.”
Spisak: Business intelligence is on its last legs as analytics shifts
“I don't want to say BI is dead, but it's, you know, it's on its last leg. I think everyone wants to move to more predictions, actionable, prescriptive analytics, and you can't do that when you're just kind of looking at pretty pictures. So I think we're seeing…”
Large data sets create a talent magnet for top data scientists
“If you're the one with the big giant corpus, you'll attract the very best data scientists because they'll want to dive into that. They'll come up with the right features and the right ideas, and that will be another sort of effect on top.”
Hiring specialized data scientists before accumulating data causes high employee churn
“I often advise companies where they say, oh, you know, we're going to hire these five data scientists, but they don't have any data yet. And what they don't really realize is that if these are, if they're data scientists who are happy to take out the trash and…”
Douetteau: Companies cannot hire a data scientist without already having one
“You can't hire a data scientist without a data scientist, meaning you don't even know what the hiring process is.”
Evans-Harris: Data scientists want to act responsibly but lack practical guidance
“Because what many of the data scientists that we spoke with just said was, it's not that I don't want to behave ethically or responsibly, it's I don't want to know what that looks like. And two, I don't know what to do if something bad happens.”
Data scientists' biggest problem is finding data
“One of their first frustrations is, I can't find the data, right? That's like their biggest problem.”
Data scientists quit when companies fail to operationalize their work
“So these talented people that you then hire are becoming demotivated and will actually go somewhere else just because their work is not actually adding any value to the business.”
Data science Venn diagrams underplay the importance of software engineering and business
“I think that's the minimum bar to be a sufficient data scientist. A, B it totally underplays the importance of software engineering, and C, that's actually just a small part of what it means to be a data scientist. I think it totally neglects things like busin…”
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.”
Startups should not hire data scientists before reaching an MVP
“Basically my advice is not to start a startup with a data scientist. You know, focus on getting to MVP, getting some traction, generating some real data, make good decisions early on, and then hire data scientists as you scale.”
Data scientists are highly-paid janitors who spend 80 percent of time munging
“Most of the data scientists that are highly paid and highly educated are data janitors. Because 80% of the time, they are spending time munging data.”
Scholnick: Data scientists dislike enterprise sales pitches as much as developers
“But I think you can build a similar business to The type of, to the direct to developer tool businesses. There's no reason why not. I don't think that data scientists enjoy being sold to any more than developers do.”
Centralized functional data teams prevent duplicate work and foster peer learning
“To organize functionally, but what we've found is that people can learn from each other really well that the data scientists and the statistical analysts can learn from each other and that that's great, and that it really helps when people are working together…”
Birchbox defines data scientists as PhD-level coders on product teams
“So we see a data scientist as someone who contributes to product development, essentially. Someone with very specialized skills, Who helps us deliver our products into market. And so by specialized skills, I essentially mean a PhD or something very much like i…”
Non-scrappy data scientists do not belong in early-stage startups
“I wouldn't hire someone that couldn't go out and be scrappy and get their hands dirty. That person doesn't belong in an early stage startup anyway.”
Jake Porway: Placing data scientists in non-profits fails without structural support.
“Data scientists are expensive, and even if you just plopped a data scientist in with a lot of these non-profits, they wouldn't know what to do with one.”
John Rauser: Working data scientists must know how to code
“Yeah, I mean, I think, ah, like a working data scientist has to be able to code, because if they can't fish for themselves, right?”
Robbie Allen predicts data scientist roles will largely disappear by 2024
“In five to 10 years, the role, at least the role of data scientist or data engineer will largely go away, or mostly change, and it'll turn into programming systems like ours.”
Context Relevant positions its product to boost productivity, not replace data scientists
“And so, we actually do not sell our technology as a complete replacement for data scientists. We encourage you to think of it as a huge productivity game.”
Turck: In 2013, data science rapidly became a highly sought-after job
“The term sort of didn't exist a few years ago. It appeared, and now it's one of the most, ah, sought-after jobs”
Wiggins: Data science hiring should prioritize listening skills over domain expertise
“So, I think what you're looking for is not a particularly somebody with a domain background, but somebody who's proven themselves to be a good listener.”
Gupta: Most companies misuse data scientists by treating them like plumbers
“Because the problem you have, you know, a lot of companies have hired these years as scientists. They take what I call as a scientific artist, and they convert them into a plumber.”
Cathy O'Neil: Data scientists must reject roles that treat them as implementers
“When mathematicians ask me how do you become a data scientist, I give them a lot of advice, but one piece of advice I have is if you interview with a business who thinks that you're an implementer and not a business person, then don't take that job.”