Background: I currently lead a Data Science team at a big non-tech company. Previous to this I worked at a software company that had a Data Science team in th

Background: I currently lead a Data Science team at a big non-tech company. Previous to this I worked at a software company that had a Data Science team in their customer facing consulting group. I'm going to speak primarily about applied data science. This means a data scientist who is solving a business need by doing ad-hoc analysis or building a reusable solution (e.g a R+Shiny dashboard) to a business problems. Jobs: There are plenty of jobs out there, but you have to be careful. Many "Data Science" jobs are really BI, Business Analyst, or Sales Engineer types of jobs where some VP got it in their head that they need a Data Scientist. These jobs are great for people who are okay with Technology and Data Science being 10% of their job - and many people are like that. They don't care about engineering, coding, or tech and statistics beyond the minimum to do their jobs. But if you really want a job that involves solid tech and stats/ML skills you will be unsatisfied at these types of jobs. Right now there are plenty of hard business problems that people want to turn into Data Science problems because they think it'll give them a competitive edge or something to market and show off. This results in more data science job openings. However, they are not really data science problems. As somebody else said, people will eventually realize they are not getting the value they need with data scientists doing these types of jobs. Then they'll replace that person with an MBA with some DS coursework (e.g. MBA who can use KNIME or SAS Enterprise Miner) or eliminate the position. People: I interview people and I know people at other organizations who interview candidates for Data Science roles. MOOCs and many degree programs (including 2 year MS degrees) are pushing out people who have a very superficial overview of data science. Basically they teach them about every ML algorithm in the known universe and the functions to call them them in R/Python/SAS. The end result is a mediocre coder or non-coder who boils everything down to a confusion matrix or root mean squared error. But they cannot actually think through a business problem or see why a low error doesn't equal a good model (see http://www.tylervigen.com/spurious-correlations http://www.tylervigen.com/spurious-correlations) Finding good people is hard and you have to be flexible to realize great people can come from different backgrounds.