I've been in different Data Science roles. For most of them my output was code and code going to production (which meant different things in different places).

I've been in different Data Science roles. For most of them my output was code and code going to production (which meant different things in different places). For one of my jobs my output was mostly PowerPoint and I left that job quickly because of what mattcdrake discussed. I was also in Data Science consulting for a software company for a long while, so I got to see what a lot other Data Science teams were doing. One huge challenge for many Data Scientists is that they have a lot of incompatible expectations from all the different teams they work with. The stakeholder wants a model in 3 weeks, but doesn't understand what a model is what it means to build one. The executive wants to see ROI on their Data Science Investment because it's built into their 12 month financial plan. The Data Engineering team provides access to the data, but don't have the resources to help with all the data issues you are seeing. The Subject Matter Expert is trying their best to help, but they don't understand why you can't easily implement what they are saying. The project manager wants an estimate, but you have no idea how fast any of these other parties are going to move or issues are going to be resolved. Then there is your manager. How do they evaluate your performance exactly? Models deployed per month? Reports per week? Data Issues fixed a quarter, even though nobody but you seems to understand why those issues need to be resolved? What if you do a lot of great work but the model sucks anyway because of stuff out of your control? As a Software Engineer, your manager can still say "great job" when you ship, even if it fails with users. Nobody blames a Data Analyst if a report they create shows something is bad. It's hard to feel satisfied in a job where you face these conflicting expectations. I think in other roles (e.g. Software Engineer, Data Analyst, Data Engineer), the expectations are more consistent because you intersect with fewer people and your work is better defined. Even if the metrics are crap (e.g. Lines of Code), at least they are consistent. I'm not unhappy in my current Data Scientist job. I'm also a bit older and jaded, so to me "not unhappy" is pretty good. One of the big reasons I'm not unhappy is because my output is code and not slides/reports.