Data Science

“So I don't know what the future of data science looks like, but I think as a product manager, I've spent less time in the last year talking to a data scientist than I ever have in my career, even though I've probably spent 10 times more time in data and unders…”

“Most of AI is just glorified data science.”

“I actually think some of our strongest product staff are going to be converts from design and from data science who are just looking to expand their reach.”

“Meanwhile, I think a lot of people going into product might be coming from design, might be coming from data science, might be coming from engineering because the ones that have judgment, the ones that can talk, the ones that want to stay current, they might b…”

“We now have, I don't know, maybe 20 or twenty-ish, 20 to 30 people, something like that, in data science that are just processing different types of data and alternative data.”

“In the same way that folks with data science backgrounds and AI knowledge are super in demand, the software programming space has hit a little bit of a soft patch. There's a lot of opportunities to hire great engineers these days.”

“People say the word eval is trying to kind of like carve out this new thing, and saying, you know, evals, and then A-B testing, but if you zoom out, it's the same data science as before, and I think that's what's causing the confusion is, hey, we need data sci…”

“People Kind of go through the same iterations. New people come to the space or possibly maybe it's more the business side get really excited by the prospect of what AI or data science or neural networks, whatever iteration we're in can do. And then we kind of …”

“It should not be solved by adding more data scientists, because anyway, there's not enough data scientists, and if they don't understand the business, there's no point. It should be solved by finding a way for people from the business that are very much into d…”

“One, generally speaking, don't listen to data scientists. Data science is mostly bullshit. Health papers and stuff like most of them are just total garbage.”

“I think a lot more data science is going to come in versus machine learning engineering, because a lot of it now is just quantifying, like, what does the business actually want as an outcome, right?”

“They were like, listen, we have all these different people throwing insights over the transom. And it's great. We want to hear from the data scientists, from the product specialists, from the customer service people and the voice of the customer, whatever, all…”

“It's a staple of data science, ok, that 65 to 70% of any given situation that you're trying to model is externalities, or let's call it the wave that you don't control, but you are seeking to surf.”

“Every single thing I just said, finding potential matches, making matches, and then learning about those matches, and then, you know, cycling back again, that is the data science of marketplaces. And I feel like every marketplace that you could think of, you k…”

“It's basically because if you're measured narrowly on impact and that's all anyone sees around you, then it's very hard to engage with the creative aspect of business change and the strategic aspects of business change.”

“What AI has done for us is it's massively expanded the frontier of things we could think about our problem, hypotheses we could have, maybe things we could test. It's just an astronomical explosion of explanations and ideas and principle. And I really think ac…”

“In the same way that AI generates a lot of ideas, AI also generates a lot of pros. And in data science, that can actually be deadly because you're getting more explanations that sometimes maybe are extraneous, you know?”

“As soon as you centralize something, you will inevitably lose the deep expertise you can get from embedding, but as soon as you embed everyone, you lose that, like, you know, Center of excellence where everyone comes together and you set standards for your dat…”

“I see more representation in, in data teams than I do for something, let's say like the, I don't know, hardware level programming. You know, which has just historically been, like, a lot of these, like, deep nitty-gritty computer science fields have been, like…”

“I grew up as a physicist thinking that, you know, physics really won World War II, but now that I'm a data scientist, I realize that it was actually data science that won World War II, but that story was classified for about 75 years, which is the story of how…”

“I think when you look at all the analytical skills, whether, and all the analytical professions today, data science, statistics, AI, ML, Product management, for example, and so on. All of it has math thinking at its core. If this is a solar system of skills, t…”

“I think one of the big changes would be, we'll move from this specialized and specialization to generalization. So some of the things that we consider specialization today, like data engineering, a large portion of what we call data science becomes basic engin…”

“The original quote unquote data scientists were actuaries, right? So these were people who were looking at mortality tables, morbidity tables, dating back hundreds of years, right? Because insurance has been around for hundreds of years and creating models to …”

“Pretty much everything that humans are doing in these offices today, that's very repetitive, and it's not extremely creative, can be automated. So I think the impact of data science and AI is going to be massive, definitely in the trillions of dollars, in term…”

“And eventually financial services is not about great technology, it's not about great algorithms, it's not about great data science, it is simply about great product and great service, right?”

“Data science is not actually a job or a role. It's a literacy”

“Though ostensibly duality technologies is really about security and cryptography, we're actually very much heart and soul at, a data science company, and how we can enable analytics on data that is protected and sensitive.”

“I think the biggest misconception here is that the only way to use data is like the way that machine learning or AI works. You know, people overlook things like good old-fashioned accounting. You know, accounting was sort of the very first data science.”

“And I think that's sort of a neglected part of data science. Everyone talks about, like machine learning and building all of these data products, but I think lots of the impact, lots of the trust and usage of data actually comes from being able to answer to so…”

“There's folks who build models to help machines make decisions, and there's folks who, like, build charts and help people, humans make decisions at a much lower frequency.”

“We see ourselves very much as a data company, and we thought that data science in the early days would be a really key part of our strategy, and it is, but the data engineering challenge is so immensely harder that kind of data science kind of, in terms of tea…”

“You know, I think that data science will become the new academic approach in computer science. There'll be less coding and more about data science. New algorithms for data science, new Approaches to understand the world around us. So, you know, we can call thi…”

“Pharma companies will start to view themselves more as data generating companies and data science companies.”

“Capital One, who is, I would say, the original data science company, figured out tens of billions of dollars of business, giving credit cards to people who others had denied, and making it profitable using Data science.”

“So I guess that it will also get to global manufacturing in the next 10 years, probably.”

“All data science And all AI and all ML is fundamentally vertically specific. Which is the dirty secret about AI and ML powered businesses. They are fundamentally vertical problems.”

“At least in software, enterprise software, the value of data science is, is hugely valuable as a source of differentiation. But it's only valuable if presented along with either a workflow problem or an infrastructure problem.”

“Because it helps us to create this vocabulary of risk. It's a common language that enables us to have practical conversations across entire organization, whether you're a security practitioner or engineer, or you sit on the board or the audit committee to disc…”

“So when you look at data science in general, Across any vertical, doesn't matter, you can pick a vertical, the problems or the challenges, they don't like when you say problems, the challenges that we're solving are unanimous, right? If you're looking at healt…”

“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.”

“So if you have to make a career choice to study data science today, I would rethink that. And I'm serious.”

“And, ah, the shortage of data scientists will be solved by a combination of pragmatic education, practical education, and, ah, tools that automate the modeling process and automate data science to levels which are, right now, probably most of us in the room th…”

“People have tried many, many times to use data science and other things to try to quantify these kinds of questions you're asking and the results have been pretty, pretty poor. It's very, it's been very hard to predict these things.”

“Every task that you work on has kind of different different features work better or worse, and I spent, when I first was working as a data scientist, I spent all my time on this feature selection, and as you guys know, you try a hundred things and maybe five o…”

“If you're hiring a bunch of data scientists and then making them dependent on a different organization for engineering is basically Hiring some really expensive people, and then tying their hands behind their back, and you know, that's not a smart thing to do,…”

“The thing that makes data science different from machine learning is not just getting epsilon better predictive accuracy on learning, you know, cat's faces from pictures. It's this thing where you interact with somebody from a different discipline, and then so…”

“The data science product engineering trifecta was just a very, very powerful combination. And when you were lacking one, it didn't, it was unstable.”

“If you've, if you're working in a, if you're doing data science and you need a good distance metric, this, look at the earth mover distance. I had never heard of it before, and it's a brilliant, brilliant piece of science that's, of course, decades old.”

“Your goal is to go get everything a hundred percent right and then publish, and if that takes a couple of years, then you take a couple years. Ah, in our industry, in, you know, in industry, clearly that's not gonna work. Ah, you wanna probably get to, like, 8…”

“Data scientists as a job does deserve its own job title because these three things in one professional is new.”

“So at least in my practice of data science, the vast majority of the math we did was quite simple.”

“If a model's not working out, you should always ask yourself, can we just change the business? You know, as opposed to somehow optimally solving this problem, can we just stop doing this thing I'm trying to solve?”

“I think a lot of bad data science happens because people are like, I don't really know how this algorithm works. I'm hoping when I press this button, something good comes out. And look, it converged, so it must be okay.”

“I feel like the people who I've seen hired in as PhDs often have a sense of entitlement about their, how much they're going to have to be able to work on their own problems at the exclusion of the needs of the company.”

“There is a lot more to be gained, especially in data science, for having done projects than having necessarily spent two or three years going in depth on one topic.”

“So I don't think it's necessarily that you start with a question. I think you should start with a need. You should first trigger out, what is the problem I'm actually trying to solve before I do anything else?”

“I think of data science as And the general modelization of everything in sight, including education, including getting a job insurance, health, it's a war. And we're losing. Like we are, this is a war of the people who have money and can go hire data scientist…”

“I think that a lot of people who are interested in data science will, Decide that, you know, I gotta, I should go take a bunch of courses and read a bunch of books, and check a series of boxes, you know and I think that's actually the wrong approach. I think t…”

“I tend to think that data science exists in the middle of all of these things. So math, statistics, computer science, and the ability to write algorithms, engineering, and then hacking”