3rd wave of data scientists

Key Takeaways

so pivoting back now into this hiring and what was going on there I I thought it was really interesting in your blog how you talked about the third wave of data scientists can you go into that a bit more yeah yeah I I think I'm in a position where I can answer this as well because I feel I've been part of like oh every kind of wave if you want to call it with that so I think this all starts to make sense after there around 2011 or something especially like okay we always had like the pioneers that you know Google Facebook Twitter whoever we were like earlier doctors in early like you know the the adopted machine learning first and were like on the forefront but I guess the rest of the world was a little bit using various proprietary kind of tools doing very similar work but it was kind of all locked in and and pretty like not really it was opaque kind of kind of world and I guess that's the first wave in a nutshell and then this big data like revolution happened and particularly where I think the second wave kicked in was juked and in other books so that was just a profound moment in the dots I the data science like the science like community and it just opened up like python and like data science to to the whole world it was like this this is this brilliant moment and everyone suddenly became I would say including myself everyone suddenly became like a data scientist and there was massive like excitement and massive hype of the like possibilities that machine learning and data science can bring to to enterprise the business and I think we were rolling in that kind of wave for maybe three or four years but in the end I think businesses didn't really know how to extract business value a lot of business value out of out of kind of you know data science joke the notebook data science as it was back then so you ended up with situations where yo you know it was just like shelfware powerpoints things that were created in a vacuum and were never put in production and I think there was there's a lot of that in in business during the second wave so it was kind of a high time but a time where business value wasn't optimally extracted and I think the lesson learned there was like the data scientists cannot just be people who know build you know right Jupiter notebooks in a vacuum they have to be involved in the end-to-end process of actually prototyping something and then ensuring there gets pushed into production and I think there's a recognition amongst like businesses nowadays that this is kind of you know this is kind of a given this is kind of a requirement nowadays for the data scientist or you know she letting engineer whatever kind of kind of jobs so yeah working in a vacuum in Jupiter notebooks is not an option I think anymore and the whole industry is coming to that realization and I think that's the third wave of like the full stack kind of data scientist