Now I don&#x27;t track offers (I get contracts through recommendations&#x2F;networking), so I may be not up-to date. My background is different (PhD in quantum physics), so for me stats&#x2F;data&#x2F;ML is simple, but software architecture, algorithms - not as much.<p>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).<p>Look at <a href="https:&#x2F;&#x2F;medium.com&#x2F;@rchang&#x2F;my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.kx5dz2iud" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@rchang&#x2F;my-two-year-journey-as-a-data-sci...</a> - the taxonomy of &quot;Type A Data Scientist&quot; vs &quot;Type B Data Scientist&quot; is useful. You want to apply for the &quot;B&quot; or even - software engineer in a company which deals with data and is open to shifting roles.<p>Going back to the interviews: I see that the set of questions is entirely different. E.g. if the first question is &quot;how to invert a binary table&quot; or &quot;how to test if a black-box number generator is fair&quot;. But sometimes it is not clear from the job opening.<p>EDIT:<p>If you are interested in my background: <a href="http:&#x2F;&#x2F;p.migdal.pl&#x2F;2015&#x2F;12&#x2F;14&#x2F;sci-to-data-sci.html" rel="nofollow">http:&#x2F;&#x2F;p.migdal.pl&#x2F;2015&#x2F;12&#x2F;14&#x2F;sci-to-data-sci.html</a>