Pros
* Work/life balance is fantastic. I've never had a PTO request denied. * Coworkers are genuinely nice people. * PMs are very good. They're technical and can write basic queries and understand the basics of experimentation and machine learning. They are also incredibly knowledgable about the product they own.
Cons
* "Not built here syndrome" means that you will often use rickety internal tools to perform standard tasks instead of external tools * The performance evaluation process changes practically every quarter in the middle of the quarter without notice, often to weaponize it against employees * Reorgs just about every six months, requiring teams to totally change their priorities * Career development opportunities are overstated. You will get - at most - minimal experience with what you want to learn unless you totally transition to a new job title. * No room for high risk/high reward projects. * Teams are very territorial. It's not unusual to work on a major initiative for six months, then have another team claim ownership over it and not get to finish the project to completion. * Indeed's culture treats Product Scientists as less important than Data Scientists. Data Scientists often do Product Science-related tasks, whereas the reverse is rarely true. Product Scientists are generally assumed to be poor programmers and are not given the opportunity to write production code even when asked.