it depends if you want to be a data engineer (lots of need for that), software engineer that developers code for data scientists (Software Engineer: Machine Learner - also lots of demand), or a data scientist.
the latter, 'might' be a challenge. I started looking into linkedin profiles of data scientists of top tech companies after I realized there were more wharton mba's as data scientists than there where people who mastered in CIS in my program.
So far, 50% had a masters/phd in statistics, 25% Information / Data Science, 25% business background. However, I am still looking into it as my sample is small and I have selection bias in my sampling.
That's probably because "data scientist" isn't a job description, it's a trendy title for a whole constellation of jobs that involve looking at data. I would expect that the Wharton grads are probably doing work that is fundamentally different from most people with hardcore academic stats backgrounds.
I think this sentiment is roughly correct. It's basically the same for programmer as there are many jobs that involve programming. Note that data science doesn't necessarily mean machine learning, and there are lots of data scientists who do powerful work with stats that doesn't involve anything that looks like ml.
The jobs for people working in machine learning tend to fall into: software engineer (machine learning), research engineer, and research scientist.
Yann LeCun has an excellent Quora Q&A where he delves into the differences of the roles. I do not have the link on hand but it is probably easy to find.
the latter, 'might' be a challenge. I started looking into linkedin profiles of data scientists of top tech companies after I realized there were more wharton mba's as data scientists than there where people who mastered in CIS in my program.
So far, 50% had a masters/phd in statistics, 25% Information / Data Science, 25% business background. However, I am still looking into it as my sample is small and I have selection bias in my sampling.