Robert Chang, a data scientist formerly at Twitter and Airbnb, writes career advice and beginner guides on data science and engineering.
HN links it in threads on data science careers and job markets; commenters discuss the analyst versus builder split and ML engineer roles.
As promised, I wanted to give some details about my specific situation without polluting the main post. I am currently looking for a permanent position in the data science / data analysis area. I have specific expertise in time series analysis and by way of interest and skillsets cluster with the so called "type A" data scientists (https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.ulm7qgdcb). My search also represents an effort to transition out of academia (short postdoc after PhD) and into industry.
There are a handful of companies that I am very excited about (some of them with employees lurking here on HN). I have had a few rejections at the resume screening stage from companies advertising positions for which I had imagined there would be a strong fit. Of course, it is easy for me to imagine things that might be hard to HR staff to see in a resume. So it occurred to me that if I could establish some kind of basic social rapport with someone at one of the companies I am interested, that could potentially lead to the right set of eyes being cast on my resume in particular, but also, incidentally and perhaps more importantly my contributions to the companies project.
Finally as a side note, I feel compelled to say that I don't have an especially active or extensive github profile (as I mentioned before I am mostly a type A data scientist, and spend much more time doing exploratory data analysis and the like then writing code),…
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 - 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
Something I've run into is that a while ago, there were "Type A" ("Analysts") and "Type B" ("Builders") data scientists [1] , but most job postings now are just looking for "Type A" data scientists, and the "Type B" openings have been renamed to "ML Engineer" or "Data Engineer."
Took me for a bit of a spin because when I was first interviewing this year, I'd apply for DS roles and only get interviews that were very stats heavy with a leetcode easy, but started getting further when I basically stopped applying for DS roles and went straight for MLE roles.
[1] https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6