IMO this isn't a bubble. What people are seeing is an immature and ill-defined field that wants very technically mature people with skills that 95% match specific jobs. I think a lot of organizations don't even understand what value "Data Science" adds, so they don't know how to staff their org with Data Scientists.
I work in a data science (software company) team and it's astonishing to me that a) Our technical skills vary greatly from person to person and b) we have no culture of mentorship or training - Management sees no room for fresh grads even though all the technical people do.
I visit customers a lot and talk to startups (including applying for jobs). It quickly becomes apparent that most places are like where I work. Since "Data Science" is so new they want people who can immediately come in and show leadership in a vertical/horizontal, subject area (e.g. click-through analysis, fraud analysis) with very particular personality traits and technical skills.
Many positions also have contradictions - we want very experienced people who can work 60+ hours/week and travel 50%. Except very experienced people have other commitments and are going to be hesitant to commit to these types of positions. IMO a fresh college grad attached to a mentor is a great fit for this.
A lot of Data Science jobs are in startups or small companies - so again - little room for mentorship, training, and development since there is no time and people to do it.
Compare this to software (e.g. Java) development. It's been around for decades so there is lots of room for Juniors, Seniors, and a culture of what it means to go up the experience and skillset ladder. The value they add to and organization is also understood.
My take away from the article you linked to is "We want somebody who can start the job at 100%. But somebody else should have taken the risk and paid for the training, guidance, and mistakes. So we only take risk in the interview process."
I think in 5 - 10 years both the technology and the field will mature enough that many organizations (big and small) will have a data science team delivering defined and understood value. By that point the "Data Science" hype will have also died down and they'll be a more stable culture of what to do with juniors, seniors, hiring, and training.
I'll provide some examples and the travel requirements I've seen. Then I'll add a caveat due to my own sampling bias.
1. A lot of companies have a small or no data science team. So a lot of work is contracted. Contractors can be at 75%+ travel if they are not local.
2. Companies that sell software with "advanced analytic" (e.g. R) capabilities that is customized for customers. There are also companies that sell prebuilt solutions for specific use cases (e.g. oil production analysis analysis) where the core R/Python scripts are already built, but they have to integrate with customer's IT infrastructure and validate output. Closer to 50% travel (this includes sales and services).
3. Some large (e.g. 30,000+ employees) companies have data science teams that are tasked with helping the rest of the company run better. They don't focus on one area. Rather, they visit different divisions and evangelize and create data science for better operations. They operate almost like internal contractors when conveying their value to upper management. Travel up to 25% since different divisions can be spread across a country or even the world.
Now my caveat. Most of what I've described are describing positions for data science generalists, not experts in one specific area (e.g. looking at price elasticity in consumer goods). From what I've seen on job sites, a lot of more focused data science positions are in advertising (or related) and deep data mining of people. I typically don't focus on these jobs so I'm missing data on a lot of 0-5% travel positions.
Also keep in mind that many companies want guidance. So even if you are an expert in a highly focused subject area in a role that isn't customer facing, there is value in getting you out there to talk to people about how to do things and best practices.
