Pros
Great compensation, good flexibility, incredible philosophy, strong top down leadership, great tech stack. If you’re happy and efficient at building data pipelines and dashboards , this could be a great fit (not sarcasm, genuine statement!).
Cons
Day to day work is more product analyst than data scientists- building data pipelines, setting up eventing ingestion and wrangling, and creating and maintaining dashboards. There are a lot of requests to track model and track user behavior, but most commonly at a click modeling level. Deeper analysis and more advanced modeling weren’t common and, when they are, don’t go very far in terms of implementation. It is a data heavy culture and the tools are great for achieving that. The problems just tend to be very shallow. There is also a lot of pressure to “ship” and have business impact, which can put you in a bind with balancing superficial, urgent stakeholder dashboard requests and having broader systems level impact that may take more iterations to achieve. This results in more marketing than impact at times, because there’s not a sense of security to support longer running projects.
