If data science is in demand, why is it so hard to get a job?

If data science is in demand, why is it so hard to get a job?


Another question recently posted on social networks. Below is my answer, with link to the original post. 
The main reason is the exponential growth of data science candidates, while the growth in job openings, even though exponential as well, is increasing at a lower pace than the number of applicants. In some ways, this is similar to the explosion of PhD people, while the number of jobs for these people is shrinking. One would wonder: why so many people want to get a PhD when job prospects are not, by far, what they used to be? I think the answer to that question also applies to data scientists; there are a few similarities:

Four years ago, when companies could not find any real data scientist, these companies — helped by academia and data camps — managed somehow to create an explosion of candidates to fill the slots.
It became some kind of Ponzi scheme, where nowadays, many so-called data scientists have no other option but offering services to train people interested in becoming a data scientist. The same is true for PhD people: many earn money writing someone else’s PhD thesis. Companies are now very careful about assessing the value of someone self-calling herself  “data scientist”, can bring.
Just like many PhD people, especially new ones who get their PhD producing very little original research, the value of their degree has declined. Those from top universities, and this also applies to data scientists, are well equipped and have no problems finding a fantastic job.
People with just a few days of training will have a hard time getting a job. Yet some people with no official training in data science, geographers, engineers, or physicists with substantial professional experience working with data, can still find a new job as a data scientist (though their job title might be different) in no time.  Same with many new graduates who accept an internship as the first milestone in their career.
There are so many people calling themselves data scientists today, usually calling themselves “data science enthusiast”, and with no experience, that it is not a surprise few can get a job.
You can get a job (internship) when companies visit your campus and talk to you. Far more efficient than sending resumes over the Internet (aka “black hole”.) Or you can smartly interact when you see a Facebook ad recruiting data science engineers, and post some great comment, rather than using a passive approach. Resumes are getting so passe, maybe one day no one will use one anymore. It is the case for me. Why not posting some of your contributions on Github instead (or on our website) — this will give you far more visibility, if the content is of high quality, gets accepted, and become popular.
About 90% of the people who want to connect with me on LinkedIn, I have to decline them because they are irrelevant to data science. Since, like everyone, I am limited to 30,000 connections, I can only accept an handful number of new connections. Same issue with companies, as they have a limited budget as well.  
The situation could be far better or far worse, depending on where you are located, and your salary expectations. While some data scientists (usually managing a large team) are paid over $250k in US, and others, managing their own successful company, well over $500k, those numbers are exceptions. And if you don’t succeed (produce value) at that level of compensation, you will be downsized in no time.

Below is a made-up chart displaying the distribution of data scientists, in terms of value added if hired, posted here. 

My 2 cents.
Vincent
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For related articles from the same author, click here or visit www.VincentGranville.com. Follow me on on LinkedIn, or visit my old web page here.
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