3 ms·

Likely. Data analysis (of which ML is an important part) is needed in many places, from entry-level to top-level. I am a data science freelancer and I mostly d

by stared 11y ago

Likely. Data analysis (of which ML is an important part) is needed in many places, from entry-level to top-level. I am a data science freelancer and I mostly do projects for IT-dominated companies. First, I was surprised that such companies need some external help with relatively simple tasks; only later I discovered that top-notch performance in webdev (or even: algorithms) does not mean that someone is able to do simplest data analysis. For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis. When it comes to deep learning in particular - I don't know.

zo1 11y ago
>"For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis." I've been wanting to get into this field recently. Do you have more info about these openings, perhaps?
- stared 11y ago
  Now I don't track offers (I get contracts through recommendations/networking), so I may be not up-to date. My background is different (PhD in quantum physics), so for me stats/data/ML is simple, but software architecture, algorithms - not as much. When 3 years ago I was looking for data science internships, most of interview were strictly in software engineering. (I got into a more data-analysis oriented.) Even when I applied to Google a year ago (and failed), all non-trivial questions where in software engineering (some with data-oriented paradigms, tough). Look at https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.kx5dz2iud https://medium.com/@rchang/my-two-year-journey-as-a-data-sci... - the taxonomy of "Type A Data Scientist" vs "Type B Data Scientist" is useful. You want to apply for the "B" or even - software engineer in a company which deals with data and is open to shifting roles. Going back to the interviews: I see that the set of questions is entirely different. E.g. if the first question is "how to invert a binary table" or "how to test if a black-box number generator is fair". But sometimes it is not clear from the job opening. EDIT: If you are interested in my background: http://p.migdal.pl/2015/12/14/sci-to-data-sci.html http://p.migdal.pl/2015/12/14/sci-to-data-sci.html

by stared 11y ago

zo1 11y ago

>"For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis." I've been wanting to get into this field recently. Do you have more info about these openings, perhaps?

stared 11y ago
Now I don't track offers (I get contracts through recommendations/networking), so I may be not up-to date. My background is different (PhD in quantum physics), so for me stats/data/ML is simple, but software architecture, algorithms - not as much. When 3 years ago I was looking for data science internships, most of interview were strictly in software engineering. (I got into a more data-analysis oriented.) Even when I applied to Google a year ago (and failed), all non-trivial questions where in software engineering (some with data-oriented paradigms, tough). Look at https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.kx5dz2iud https://medium.com/@rchang/my-two-year-journey-as-a-data-sci... - the taxonomy of "Type A Data Scientist" vs "Type B Data Scientist" is useful. You want to apply for the "B" or even - software engineer in a company which deals with data and is open to shifting roles. Going back to the interviews: I see that the set of questions is entirely different. E.g. if the first question is "how to invert a binary table" or "how to test if a black-box number generator is fair". But sometimes it is not clear from the job opening. EDIT: If you are interested in my background: http://p.migdal.pl/2015/12/14/sci-to-data-sci.html http://p.migdal.pl/2015/12/14/sci-to-data-sci.html