| This reads as a series of bad job experiences and I think is explained by a wide variety of job functions that all can have "Data Scientist" as a title. Someone else's experience could be totally different. You have to know what to look for and what to avoid. If you're trying to find a DS job, one of your top priorities is finding out what the actual job consists of. For instance, a Data Scientist at Facebook might be called a Data Analyst at many other places--no modeling required. I know this because I've been on that journey. But there's no reason to expect some department head that's never been exposed to DS to know this. They just copy/paste some other company's job req. If you're more junior, here are my tips: - If it's a "new DS team" that supports a variety of teams: beware. Bolt-on DS doesn't work well, as it's really hard to build a meaningful solution that's not deeply integrated. - If it's an old company or in a conservative industry: beware. There are likely to be data silos and difficult ownership models that make it nearly impossible to get and join the data you need. - If it's a small company: beware. You're likely going to need a broad set of knowledge that's won with several years of experience to be able to build end-to-end solutions that are integrated into the rest of the tech stack. - If it's not an engineering-driven culture: beware. DS will often be used to provide evidence to someone else whose already made up their mind and pretend they're being data-driven, and you'll be the disrespected nerd that's expected to do what it takes to deliver the answer they want. Most companies claim to be "data-driven", few are, and even fewer understand data-driven isn't always possible or desirable. Industry is still trying to figure out how to use ML and are still learning that it's not as easy as hiring someone that knows about all the algorithms, but rather it takes deep technological changes to data infrastructure to enable the datasets that can then be used by the ML experts. But you don't have to be the person that helps them figure this out the hard way (i.e. by being paid to not accomplish much due to problems outside of your control). Better to find a place with a healthy data science team that can help you learn and contribute. They exist. |
I'm at a place that is both, and both are huge pains.
On the engineering side, it's a bit different though: technical roles are looked down on, and there is no engineering culture, eg, for data. Data is just a bunch of flat files everywhere, across many silos. No leadership to put it together into logical buckets for easy access and interoperability