Career advice for new grads or early career data scientists/analysts looking to ride the AI wave
Our take
From what I'm starting to see in the job market, it seems to me that the demand for "traditional" data science or machine learning roles seem be decreasing and shifting towards these new LLM-adjacent roles like AI/ML engineers. I think the main caveat to this assumption are DS roles that require strong domain knowledge to begin with and are more so looking to add data science best practices and problem framing to a team (think fields like finance or life sciences). Honestly it's not hard to see why as someone with strong domain knowledge and basic statistics can now build reasonable predictive models and run an analysis by querying an LLM for the code, check their assumptions with it, run tests and evals, etc.
Having said that, I'm curious what the subs advice would be for new grads (or early career DS) who graduated around the time of the ChatGPT genesis to maximize their chance of breaking into data? Assume these new grads are bootcamp graduates or did a Bachelors/Masters in a generic data science program (analysis in a notebook, model development, feature engineering, etc) without much prior experience related to statistics or programming. Asking new DS to pivot and target these roles just doesn't seem feasible because a lot of the time the requirements are often a strong software engineering background as a bare minimum.
Given the field itself is rapidly shifting with the advances in AI we're seeing (increased LLM capabilities, multimodality, agents, etc), what would be your advice for new grads to break into data/AI? Did this cohort of new grads get rug-pulled? Or is there still a play here for them to upskill in other areas like data/analytics engineering to increase their chances of success?
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