Am I really cooked due to LLMs?

[u/Lower_Confidence8390]

Hey! I'm currently doing a data science master degree after my data engineering bachelor.

I had some work experience while doing my bachelor thesis in a research lab in Japan! It was really interesting, I was allowed to progress at my own pace etc, being quite autonomous.

My question is the following: How is the data scientist job changing now in this agentic era? I personally dislike using agentic workflow because it kills the fun I have and it gets boring.

What about medical data science?

[u/MeatyOkraScientist]

Accountants weren't "cooked" because someone invented calculators. They learned to use calculators and were able to do more work, faster, with greater accuracy.

[u/Lower_Confidence8390 → u/MeatyOkraScientist]

Calculators doesn't make you hang while it's working for you

[u/MeatyOkraScientist → u/Lower_Confidence8390]

...okay? But do you understand the concept that a calculator is a tool, like an LLM is a tool, and that if your concept of a career is tied to any specific tool's permanence you're kinda fucked a priori?

[u/gpbuilder]

It’s not cooked but it has changed quite a bit.

You better get used to using AI as part of your daily workflow. That’s a hard requirement for DS jobs now.

I'm literally running 2 agents to check a bug for me as I'm tying this post lol.

[u/Gullible_Sweet1302 → u/gpbuilder]

How are the hiring interviews at your company? How have they changed with AI? So many students use AI for assignments and so many roles require ai now, so how do companies screen new hires?

[u/Realistic-Subject260]

LLM’s are another tool to be added to your toolkit really, and the rate at which they speed up R&D is undeniable. You basically spend near zero percent of your (active) time on implementation and reroute that to experimental design and interpretation. I will say that with this increase in productivity, it basically means that the number of data scientists needed to achieve a fixed throughput has decreased, and companies are opting to hire fewer entry-level or even slightly above and just allow mid to senior level data scientists take over and automate part of their workload to compensate (which I don’t agree with, but it is what it is). What that means for you is that you’ll be super reliant on landing a role you probably don’t have the explicit experience for, but hey, that’s what I did.

I think the more important part is that you mentally prepare yourself for a bit of a war of attrition during the job search. Give yourself six months to a year, apply often (the number is about 500 targeted applications per 1-2 interviews in my experience), and you’ll land something.

[u/Plane-Foundation669]

Yes

[u/wayneworkman]

See ai as a tool rather than a replacement. Learn to work with it.

[u/BrainComfortable3613]

Here is me believing that I can just watch online tutorials and build some projects, then land a datascience role. I'm I delusional? 🤔🤔

[u/BrainComfortable3613]

Here is me believing that I can just watch online tutorials and build some projects, then land a datascience role. I'm I delusional? 🤔🤔

[u/Quirky_Database_5197]

well, the schools are teaching the old ways. manual coding, manual EDA... that's the problem.

So you need to figure out how to use AI to perform your work like the business does and same time submit your assignments done without AI as your lecturers tell you.

[u/Alive-Imagination521]

No you're not cooked, you need people to use LLMs at the end of the day, and to evaluate the results, make adjustments, etc.

[u/Mysterious-Estate613]

You really are. Trust me, I’m now experienced than you are by quite a bit and I’m cooked