The litmus test for me on whether we're adding adding value as an education company has always been: are there Insight Fellows who get rejected from companies X,Y,Z prior to Insight then get offers from X,Y,Z after Insight? From the very first session through to today, we have numerous examples each session of this happening.
A recent example was a Data Science Fellow who was a physics postdoc at Lawrence Berkeley National Lab prior to Insight. Right after his postdoc ended, he applied to half a dozen bay area tech companies (all the usual brand name suspects), got rejected from all of them. He came to Insight and during his fellowship built a video scene segmentation & object detection project with a YC startup. After Insight he got an offer from every one of the companies he previously got rejected from. He went on to accept an offer on the LinkedIn data science security team (which is led by another Insight alum).
We’ve seen this happen time and again on the software engineering side as well with our data engineering program. A Data Engineering Fellow prior to Insight has a generalist software engineer experience but a passion for big data, wants to do big data full-time, but no one will take a chance on her/him. At Insight they build a sophisticated data pipeline on AWS, while being mentored by leading data engineers, and then the same companies previously rejecting that Fellow for data engineering roles make offers because they now have the evidence they need that she/he can solve the types of specialized problems the company is facing.
In your experience, how much of the interviews for an AI / DL role consists of classic CS algorithm puzzlers, compared to a regular software engineering interview at a place like Google / Facebook?
I think you're partially right about these programs picking 'winners' and ushering them into jobs they could have done without this program since the actual work here boils down to a 3 week project.
However, it can be pretty hard to even get an interview if you don't fit what recruiters pattern matching against, so in some sense this is helping companies make smarter recruiting decisions.
A recent example was a Data Science Fellow who was a physics postdoc at Lawrence Berkeley National Lab prior to Insight. Right after his postdoc ended, he applied to half a dozen bay area tech companies (all the usual brand name suspects), got rejected from all of them. He came to Insight and during his fellowship built a video scene segmentation & object detection project with a YC startup. After Insight he got an offer from every one of the companies he previously got rejected from. He went on to accept an offer on the LinkedIn data science security team (which is led by another Insight alum).
We’ve seen this happen time and again on the software engineering side as well with our data engineering program. A Data Engineering Fellow prior to Insight has a generalist software engineer experience but a passion for big data, wants to do big data full-time, but no one will take a chance on her/him. At Insight they build a sophisticated data pipeline on AWS, while being mentored by leading data engineers, and then the same companies previously rejecting that Fellow for data engineering roles make offers because they now have the evidence they need that she/he can solve the types of specialized problems the company is facing.