Tech IndustryJun 7, 2019
PayPalsarcasm

How come everyone is AI, ML and DS experts on LinkedIn feeds

Question says it all. Every Tom, Dick and Harry is AI, ML engineer and Data Scientists. Folks who are really working on these stuff. Do you get to work on Maths and Statistics part of them or just another "use that library, sir" thingy? I would like to know precisely what is your responsibility in your daily work?

Facebook bookfacer Jun 7, 2019

People put this stuff on their linked in because it is in demand. Not rocket science.

PayPal sarcasm OP Jun 7, 2019

Do you work on these? Can you elaborate your job responsibilities?

Microsoft L61 Jun 7, 2019

They will claim expertise on something g else in next 3-5 years

Microsoft Vbsm66 Jun 7, 2019

There's no established minimum bar for putting that on your LinkedIn

Twitch jkley28 Jun 7, 2019

when every job requires a decade of experience in react and tensorflow, expect people to claim bullshit capabilities

Walgreens StefanoP Jun 7, 2019

You get into the math and statistics or you're not doing true data science

Daimler yrcair52s Jun 7, 2019

could you elaborate on this?

IBM 叫ēˆøēˆø Jun 7, 2019

Tweaking existing ml algorithm and/or creating new ml algorithm to fit a business need vs let me shotgun all the existing ml algorithm into this ā€œbig dataā€ and see what comes out

Walgreens StefanoP Jun 7, 2019

^ exactly To compare to software engineering, When you build software, it isn't over when you just import a library and tweak the sample applications (this is what happens when some "data scientists" approach a problem. But they shouldn't choose their framework before knowing of the problem to be solved) After you choose your software stack, now you need to actually go write the code for your particular application (in data science land, this means understanding your data through analysis, learning about the domain of the problem, etc., and verifying your solution actually solves the given problem) So it's sort of inverse to software engineering, but the core is the analysis beforehand, solution/choosing algorithms and approach to learn from the data, and analysis afterward.

Walgreens StefanoP Jun 7, 2019

Fuck this was supposed to be a comment reply. Oh well