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throw1ff t1_j0tup0w wrote

Career question here! I'd like to join ML research/engineering but somewhat struggling with my academic background (PhD in physics). I do have 8yrs solid python data stack experience, shitload of github projects, did a project in ML for my science domain and have a tailored resume for that. But I find it really hard to compete for most ML roles which are either CV or NLP and require experience at those specific areas. For software engineering in ML, I am missing infrastructure skills such as data lakes, SQL, etc. I am in Europe and would love to join remotely. Any inspiration/advice/insight from redditors?

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blacksnowboader t1_j19zld8 wrote

Maybe get a databricks free account and learn those skills about SQL and datalakes. Also, SQL is not that hard to learn. You can learn it in an afternoon.

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vsmolyakov t1_j1p0eeg wrote

I recommend building up a portfolio of CV and NLP projects on GitHub, it will help sharpen your deep learning skills. Also getting experience with model deployment and cloud certifications (AWS, Azure, GCP) is valuable for ML engineering positions.

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trnka t1_j1hbzj4 wrote

Not all positions will be open to non-traditional backgrounds, but many are. It looks to me like bigger or older companies can often look for a "traditional" resume for ML and smaller/younger companies tend to be more open-minded and focus on testing actual skills.

I've worked with a surprising number of physics PhDs in the ML space, at least in the US, so I don't see your background as a problem.

I know one of the physics PhDs did a coding bootcamp to transition into industry and that helped a lot.

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