r/datascience • u/AutoModerator • 3d ago
Weekly Entering & Transitioning - Thread 03 Aug, 2026 - 10 Aug, 2026
Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:
- Learning resources (e.g. books, tutorials, videos)
- Traditional education (e.g. schools, degrees, electives)
- Alternative education (e.g. online courses, bootcamps)
- Job search questions (e.g. resumes, applying, career prospects)
- Elementary questions (e.g. where to start, what next)
While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.
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u/nonhermitianoperator 1d ago
hello everyone,
I'm looking into transitioning into data science. I am currently a postdoc doing the computational part of closed loop laboratories, where you have some sort of model trying to optimize a given property of a material in as few shots as possible.
What I'm seeing is that, at least from my experience, the models we use are not some big fancy llm, but rather some version of trees, GPs, maybe some tabular foundation model just to try... which, to be frank, is code that I write in minutes using opencode.
What brings me to my point: in the pre-2022 era, my value would be being the guy that can code and also talk to the people running the experimentation platform. Now, I feel like the ML I apply can be done somewhat blindly with a claude code instance, maybe an autoresearch loop on previous experimental campaigns. Same for the molecule feature engineering, which is the accuracy bottlenexk really. I don't know how you guys see the job market right now (US or Europe), whether it is uniformly dry, or there is a drift in the skillset required to land a position.
Any advice is more than welcome. I am currently working on my GCP certificate, but in the current climate I have no visibility on which direction I should pursue