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I think the ML hype is actually slowing down. I've been interviewing for ML positions as a new grad and a number of companies have told me they have an excess
by benrbray 7y ago
I think the ML hype is actually slowing down. I've been interviewing for ML positions as a new grad and a number of companies have told me they have an excess of data scientists who can train the models, but a dearth of engineers who can actually scale the models up to production. Friends at FAANGs have similar stories.
- mrlatinos 7y agoMy personal experience is that most of the models that data scientists are creating will never be scalable.
- NerdyDrone 7y agoThanks for sharing! As a new grad also looking for work, maybe I'll apply to more SE jobs, fewer data science-focused jobs lol. TBH applying for jobs is scary asf.
- pc86 7y agoMaybe it's just because I've been in industry for 10+ years now, but how are you qualified as a new grad to apply for both?
- tudelo 7y agoDouble major statistics + cs? masters statistics? Seems those would potentially qualify you if you are a solid candidate?
- benrbray 7y agoNot sure why you've been downvoted--it's really tough to know what skills will be marketable in 4+ years when choosing what to concentrate on in college. In my schooling, I really optimized for the math+stats background, since I enjoyed it and thought it would help me stand out. I even took a short detour into a machine learning PhD before deciding academia isn't for me and leaving with an MS. Now I'm on the job market, and although I have modest coding/engineering skills and a willingness to learn, it's tough to find a company willing to take the risk. Guess I min/maxed a little too much. Best of luck in your search!
- tudelo 7y agomy n=1 exp is that there are tons of openings for ML engineers but they are very wary of hiring them
- deleted 7y ago[deleted]
- streetcat1 7y agoThe goal of a data scientist is not to train the model, but to find useful signals to feed the model (separate the signal from the noise). And on the front end, tie the model prediction to the business outcome and back. The rest can or soon to be automated away.