Pros
I evaluate any job on the three P's: projects, people, pay
PROJECTS - so many cool projects! visual search, NLP, marketing auctions, pricing elasticities, recommendation systems, and the list goes on. the data is incredible and
the computational resources/support are abundant. you really get to focus on the algorithms as opposed to the engineering which is nice imo.
PEOPLE - an incredible and diverse crowd. people from all academic backgrounds. lots of phds (probably 70% or so), some extremely talented and driven undergrads. a very knowledgeable crowd, but still humble and willing to coach others and be-coached by others. a really healthy acceptance of people regardless of their nationality, religion, political views, age, etc.
PAY - I had a competing offer that come in from a company in Seattle that was about 10k higher after bonus/equity are factored in. That difference is smaller now since the company's stock value has doubled and that's part of your compensation if you work full-time. I'd rather live in Boston than Seattle so here I am.
Cons
A bit confusing at times why there are basically 2 data science teams (pricing algorithms and data science) with a seemingly arbitrary division of tasks between the two. I expect that will change in the future but it's confusing to candidates to know which team to apply for since they are basically the same.
