Seems to me more of a case where companies no longer train grads, and instead tell them to work on a proof of concept on solving a hard problem without paying for it, a sad state of affairs.
I think it is absolutely immoral (and dumb from an IP risk point of view) for companies to try to get interviewees to solve real problems for free.
This is different though: this is about potential candidates finding creative ways to demonstrate their skills.
The companies didn't even ask people to do this: these people chose to take on projects that would demonstrate their skills in a new space in which which they did no yet have commercial experience. I think that's commendable and a very smart strategy.
A very competent developer who is interested in doing some ML work may have just recently read this article and gotten to work on an ML project because he feels that not doing so will hurt his chances of getting a job.
As a note, I have no idea what the right solution to this problem is, it is good to confirm candidate's knowledge (I prefer creative ways similar to the parent) and part of that knowledge certainly can include open source work, but going down this road too much leads to people feeling obligated to make open source work to put on their resume and even to people faking open source work to try and land a job.
...which makes the world a better place. Why do you object to people writing open source code?
i do have one concern if it ever became an industry prereq, it becomes a filter for those who have more free time to work on side projects.
Doing a unique project is much better for learning cost/benefits of implementing AI/ML, although this post may be overoptimistic on how that can lead to a job offer.
It seems like you would need a very specific level of knowledge to romanticize data science in that way. Most people know too little (So you're basically trying to build skynet?), and most of the rest are either in the industry or know someone that is, and so have a more realistic view.
I don't know, I did maths in undergrad, maybe some of the more clueless CS majors thought this way.
Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!).
The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact.
For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS
For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-...
Lockheed Martin CEO: So what the fuck is this youtube video?