I've never actually met someone off the internet who calls themselves a data scientist.
You could probably say the same about a lot of job titles. I've never meet a sanitation worker but my trash gets picked up once a week nonetheless.
Maybe this holds in the consulting world? It definitely does not hold in the tech world, IME.
As a new grad that went through the hunt very recently - it was a messy process. Very few places will consider you without extensive experience, or a masters/Ph.D. Of course if you're hiring people to research machine learning algorithms that's justifiable, but plenty of the responsibilities people associate with data scientists don't require advanced degrees.
And the number of posts asking for 5, 7, even 10 years of experience... absolutely astounding.
As someone uninterested in going back to school, I've resigned myself to getting some work experience and doing personal projects for 1-2 yrs before trying again.
applied to ~85 data science positions. I can't even get recruiters to call me for a phone screening, so don't feel down =)
1. Previous internship in data science 2. Experience developing R packages and putting them on GitHub 3. Really having statistical theory down pat
You don't need an advanced degree to be a data scientist but you need a strong understanding of stats and how to work with data. Having an advanced degree is a good indicator that you can do that. But it's not a prerequisite for an undergrad: Github, internships, TA-ships can make that up.
I think one advantage is that while PhD's are typically very good at the research process and the techniques used in their research, undergrads could be more flexible and adaptive to different situations.
Definitely will take note of that in the future though.
I may do a postmortem on my search later, but speaking from my experience with many, many interviews over the past couple months, the TL;DR is that the conventional interview wisdom on Hacker News/the cscareerquestions subreddit/this article is wrong and out of date. Interviews for such positions require a different set of skills than just reading Cracking the Code Interview (and ones that you can't get at a data bootcamp).
On the technical side, there is often more-advanced SQL (nested JOINs + PostgreSQL window functions). On the big data side, there is often discussion of distributed systems (e.g. Spark clusters) and practical algorithmic complexity at scale (i.e. instant fail if you suggest anything loglinear or slower).
On Hacker News, every time an interview thread pops up, there is a discussion decrying the use of technical screenings before an onsite, and often suggest practical work experience instead using a homework assignment (which this article does not discuss).
Most of the companies I've talked with for data analyst/science roles have given me both a homework assignment and a technical screen before the onsite. And often a prescreen test before both of them.
There have been a number of occasions where I easily passed the homework screen but failed the technical screen (without any feedback as to why). And it's beginning to get annoying.
Very few companies are actually using their data scientists as scientists. From my experience.Except for when I worked at a large hospital. We had a research board, and had to be certified to study Humans CITI. But beyond that..
In what way is what statisticians do not "scientific"? Setting up and rejecting (or not) the null hypothesis is the very definition of the scientific process...
