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Comment by thaumasiotes | Hacker News Reader

> They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier!

I don't really follow. Nobody thinks a guy driving a forklift is working just as hard as a guy lifting crates by hand. But he's doing more lifting! Why wouldn't you want your job to look easier? The easier it is, the more you can do.

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Because it’s bologna presented from an investor who has a huge bias in favor of their own product. Likely it was teams of data scientists who didn’t see the value add in their software. Having worked in the field, and seen plenty of ML platform pitches, they often promise much more than they can deliver and force data scientists to work in a box they have no desire to be in.

Yeah. I've seen so many vendors attempting to fix the data science workflow appear and disappear in the last decade. I'm not going to waste my time refactoring my workflow to fit some proprietary tool that could no longer exist in six months. Plus these tools are often built with an idealized conception of a data science workflow in mind, and their proprietary nature means that they tend not to be very adaptable/modular.

What if it wasn't proprietary?

If it's not proprietary and fits a need I have, absolutely! For example, Voila for Jupyter is a recent-ish tool that fits a need I have had from time to time and I will happily adopt it the next time that need comes up.

A lot of times it doesn't make life easier. It just looks easier to people signing the checks. And the people building/selling the products aren't data scientists so they don't realize they are not making life easier. They just see data scientists as a bunch of hard-nosed elitists who want everything for free.

I've worked as a data scientist on both sides of the table. As a customer facing data scientist for a product company selling to data scientists and for 2 companies that sell physical products and employ data scientists.

I've been in sales situation where I'm telling the sales rep the product we are selling isn't going to work for the data scientists we are selling to. And their response is to go above their heads because the Data Scientists "Are too technical and don't want to pay for anything."

I evaluate data science products on a semi-regular bases and many that "make my life easier" actually make one thing easier and 10 other things harder. Lots of these products are also new and they are trying to be competitive in a space that people are only starting to understand. So if something is broken I can't fix it. I'm stuck pointing fingers while my management gets frustrated with me because this solution was supposed to make life easier.

There's also the reality that right now open-source is king and if I want to hire somebody it's much easier to find people with those skills. And yes, I have a selfish desire to use open-source tools because I want to be employed in the future.

Don't get me wrong, there are product areas (e.g. Data Science DevOps) where it absolutely makes sense for a Data Scientist to buy something to make their lives easier. And a lot of them do.

The easier data science is, the more a business wants a domain expert doing it instead of a freshly graduated PhD.

The business is correct to want that. Unmotivated statistics are not good guides.

But the freshly graduated PhD does not, and is usually the person in charge of making the decision of whether to use the software initially when the field is new. Which would be a case of protecting their own job at the detriment of the company

I've personally not seen this happen. Instead, I've seen many oversold ML/AI tools that offer no advantages over using open and freely available tooling that the data scientist (with or without PhD) is already familiar with. I know this must be frustrating to ML solution vendors, but as with any product, the value proposition has to be there and easy to see for the domain expert. And the value has to be great because the downside is the vendor lock-in of the proprietary solution. Hence, ask yourself: given a green field real world ML project, would you use your tool (with the the same learning algorithms, data manipulation methods etc. under the surface as everybody else), or resort to using some battle proven free and open stack.

Imagine for a minute that you are new on Earth, first day, and you are in a car as a passenger on a highway. You watch the driver look, hold the wheel, use the brake and drive.. It seems very important what the driver is doing, so you notice many small details. However, you have no idea how much the car weighs ! or on a hill that force changes ! You watch the driver so carefully but have no idea about the basic weight of the car..

Now, you are a business person in a room with your thoughts. A presenter talks and you listen, but you watch the posture, the haircut, the order of speech .. very carefully But you have no idea about how the data moves, or the learning curve to use the tools, or even more how to innovate against "ordinary" .. you have no idea ! How could you.. so you watch the presenter carefully..

Basically, everyone has an idea of lifting a box.. so no matter how detached your life in the office is, you can appreciate a fork-lift. However, you have no idea about thirty years of *nix development, the toolkit evolution, the language wars.. etc

Here is the hard part -- many small-minded people (who run money) think NOTHING of learning.. its not important.. they care about control of the situation, and who gets the profit. Some leaders actually cultivate a smug disdain for "workers" .. some of those leaders have money.. etc...

Know this well - it is hard to believe how true it is, but perhaps you will find out over the years.

That's another perspective on bike shedding.

http://bikeshed.com/

How does your comment relate to my comment, which you're supposedly responding to?

Because the forklift driver isn't a specialist position that they will try to undercut by using the tool. If you need a full data scientists without the tool, maybe a fresh junior developer can do the same with the tool. It won't work, but that doesn't help the data scientist was was replaced along the way. Eventually knowledge builds up as to what sort of skills are needed even with that tool, but until that happens there is a chance the tool was oversold and that will result in either higher demands on the employee, an attempt to replace them, or something of that nature such less budget when growing the team.

To me, this sounds a lot like a justification built upon an bias (i.e. of course the tool the start-up was trying to sell was what the potential customers needed, it must have been those darn data scientists preventing the sales).

From my experiences at the time, there are a lot of such start-ups around, most of which did not actually entirely (or often at all) meet the needs of their customers, and were promising a lot more than they actually delivered. Whereas having data scientists in your organisation meant that at the very least, they were able to develop a proper understanding of your business, and actually create real value.

>Why wouldn't you want your job to look easier?

Automation has taught us that if your job looks easier, it takes substantially fewer of you to do it. Data scientists, in this instance, seem to be protecting themselves from what has happened in manufacturing for the last 50 years.

Well we all work with data scientists and can firmly attest this is just false.

They will work with whatever let's them focus on the actual domain problem, or a toolchain they are familiar with, or with something forced on them by org (while ranting). You know, exactly like developers

> or with something forced on them by org (while ranting).

Too true.

Was going to answer this sooner but others beat me to it! I went to a few prospect pitches where some of the newly minted well paid data scientists worked overtime to justify that what they do is unique and could not be 'commoditized' through our product. In the end pitching to business or C level worked better.

Why wouldn't you want your job to look easier?

Because you want to be highly paid?

In your warehouse example, both guys are being paid hourly, but the forklift license holder makes more, because he is clearly both more productive and more skilled.

Sure, and his job is easier and looks easier.

Also increased responsibility. You can seriously injure or kill someone with a forklift (I’ve seen someone drive the forks through the side of a fully loaded Cisco router once...)

his job is easier and looks easier.

Are you sure? Anyone can shift boxes by hand it requires no training or experience whatsoever. Whereas operating a forklift is a skill that you have to be trained in and are tested on.

Bad analogy. Data science isn't manual labor. Ask yourself, does an artisan woodworker want the shop to buy a CNC mill that they'll be obliged to use to justify its cost, knowing that they can be replaced more easily as a machine operator than as a skilled artisan?

They would probably rather advance their skills in woodworking than as a specialist in XYZ Woodco button-pushing.

Because with forklifts you need fewer box lifters. That's bad for the box lifters and so they will reject the technology.

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