Boris Gorelik AI&ML consulting
To specialize, or not to specialize, that is the data scientists' question
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In my last post on data science career, I heavily promoted the idea that a data scientist needs to find his or her specialization. I back my opinion with my experience and by citing other people's opinions. However, keep in mind that I am not a career advisor, I never surveyed the job market, and I might not know what I’m talking about. Moreover, despite the fact that I advocate for specialization, I think that I am more of a generalist.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Since I published the last post, I was pointed to some other posts and articles that either support or contradict my point of view. The most interesting ones are: "Why you shouldn’t be a data science generalist" and "Why Data Science Teams Need Generalists, Not Specialists", both are very recent and very articulated but promote different points of view. Go figure.
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“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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