overall is good, but you need know what project is best for you
Pros
- great opportunity to work with the smartest people from the world - most products have billions of users so your work have a big scope - beautiful campus, good snacks, foods, and benefits
Cons
If you are a data scientist, you need to really care about two things: - Impact. Unlike SWE, who can prove their impacts by finishing pre-planned coding projects and rolling out pre-planned features, data scientist does not automatically get credit if you only finish beautiful analyses. You have to suggest eng team to apply your suggestion from analysis, and prove how much of the product growth are from your analysis. So sometimes your impact can be subjective. - Skillset. You may be asked to do a lot of ad-hoc analyses from eng team, which I am sure are very important and can guide engineers whether to implement certain features, but that will prevent you from doing some deep dive analyses and learning some new techniques in data analysis. Maybe after a year or two, you will find that you did not build any statistical models or haven't touched any machine learning. But that's the skillset what other companies will ask for when you apply new job. Who cares you did how many ad-hoc analyses or AB test or wrote how many data pipelines. It's the fancy machine learning model that will earn you respect. Also, total pay is lower than SWE for the same level.
