I’ve noticed many 'entry-level' roles now require 2+ years of experience with specific LLM fine-tuning frameworks. Is the industry moving away from generalist math/stats backgrounds in favor of niche tool proficiency?

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Good question. I think this has always been the case for tech in general: some roles are closer to implementation and business outcomes, some roles are closer to research and experimentation. If you think about it people hiring want to take value out of the role, so unless you join a company which is investing in R&D and has the know-how and money to spend (there are companies like that out there), most companies won't be able to appreciate your background and will require you to focus on implementation and business outcomes. One of my ex-colleagues which is a data scientist had to stick for a few years with a implementation role before joining a research oriented company. It depends on how lucky you are, but I think it is definitely better to join a company and get some experience than not work at all waiting for the perfect role.

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Absolutely, context matters a lot. Early on, getting hands-on experience, even in implementation-heavy roles, often sets you up to move into more research-focused positions later. It’s about building credibility and understanding how to deliver value in real-world settings.

It’s a mix. Companies want someone who can hit the ground running with the tools they use, but strong fundamentals in math and stats still matter. The sweet spot is a candidate who can pick up new frameworks quickly while understanding the underlying principles.

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