[rafiki6]

I've seen a few similar articles now. Does this represent the general view of folks working in data science? "Data Science" is such as meaningless catch all term. The reality is in many organizations it's simply advanced business intelligence or advanced business analytics. There are some industries that lend themselves well to this whole practice, and they tend to be industries that have been borne out of the internet age (e.g. social media, internet advertising, etc.)

Some other industries have been doing "data science" for ages. Credit Risk Modelling, insurance and so on.

Every time I read one of these articles, I feel it's just an individual who entered a kind of crummy situation and they're learning what it means to work in a corporate environment. Some are better than others. Some are more motivated than others. Some have better cultures than others. Some are more willing to make technology a key part of their business strategy. Some are more data driven than others.

My recommendation is to always ask the fundamental question before joining: what are you trying to achieve with data science, and is it actually achievable?

[smeeth]

I always thought the non-specificity of the term Data Science was a strange criticism for those in the tech industry to make. How many types of SWE are there? Front-end, back-end, full-stack, devops, security, QA...

I agree wholeheartedly with your recommendation. Like any other job, each company has different needs and expectations and if you want something else out of the role you'd best avoid that company.

[rafiki6]

Frankly I have the same criticism of those who use the term software engineer. Engineering is a pretty established profession with a set of standards, ethics and practices. Most of us who work in software are not engineers. We are developers. Similarly, a scientist is one who follows the scientific method to do research. So by that logic a data scientist should be a person who uses the scientific method to do research on data. Does that make any sense? And let's be serious, is that what most data scientists are being hired to do?

[smeeth]

I'd be best described as an ML Researcher/Engineer and I'm not in the private sector so take my opinion with a grain of salt, but my understanding is that many DS roles require application of the scientific method.

A lot of DS can be boiled down to some sort of statistical testing or inference (A/B test email marketing for example) or applied ml (classification, regression). I'd argue thats science (if done right).

Data Analysis, the plot a few charts and put it in a slide deck kind? Totally agree with you. Definitely not science.

[proverbialbunny]

>So by that logic a data scientist should be a person who uses the scientific method to do research on data. Does that make any sense? And let's be serious, is that what most data scientists are being hired to do?

I certainly do, but I've been doing data science for the better half of a decade. It seems starting around 2015 when Data Science became a sexy title, a lot of fresh blood has been overgrown software engineers wanting the title, not knowing what they're getting into, or having faulty expectations. I don't consider this class of "data scientist" a data scientist, which is why the community has started shifting its job title away from Data Science to Research Scientist to better differentiate.

The good side of this is I'll come into a company with them expecting me to be like an over blown software engineer, and it gives me the opportunity to show off and go above and beyond what companies expect, allowing me to come off like a super hero. Though, it's definitely an uphill battle, and knowing how to work with upper management is an absolute necessity.

[mr_toad]

> the scientific method to do research on data

Exploratory data analysis is often overlooked and underrated.

[FridgeSeal]

Ppphhh we don’t need to do exploratory data analysis or prepare the days, don’t you know that neural networks will do all that themselves!

Doesn’t yield the right results? Clearly not enough data.

Still doesn’t work? Change to whatever the latest model google or fb is using and try again.

/s

[beckingz]

And the model will train itself right? That means that you'll have all that empty time to do more data science!

/s

[nerdponx]

But why shouldn't software developers also be engineers? Surely the difference isn't just a professional organization and accreditation.

[rafiki6]

Liability. Professional engineers are liable for the work they produce and approve. They get a fancy stamp and liability insurance and can be sued when things go wrong. That's why engineers tend to be those who work on things that can kill you. IMO if you're working on an airplane's software for example you should probably either be an engineer or supervised by one. This matters because engineering provides you guidelines you must follow and ethics you must uphold, and if you aren't following the rules your governing professional body prescribes they can strip you of your license to prevent you from being a danger to the public. There are many other critical pieces of software btw. I just mentioned airplanes because it's one of the most obvious ones.

So yes, there definitely exist software engineers who require licensing by their state or country. Most of us just aren't actual software engineers, that's my point.

[VHRanger]

A decent amount of data scientists work on AB tests which is science on production data.

Actually I'd say that's as common as the failed ML projects

[antipaul]

100% agree with the article. The top misconceptions are spot on. I’m at a big place where data science hype among leadership couldn’t be bigger.

[rafiki6]

If I may ask, what were you told when you interviewed that convinced you to join?

[antipaul]

Reasons for joining: 70% just needed a job, 20% location (silicon valley - wanted to be in tech ecosystem), 10% was combo of: I was told it's a small, entrepreneurial team with undefined remit (so opportunities to forge own path - turned out to be true) and it was impressive in many non-technical ways (company mission, campus, resources, etc)

And no reasons relating to technical or data science know-how on the part of the team/company ;) I already knew coming in that the industry is technologically backwards (big healthcare co)