Pros: Very nice team. Good work environment.

Cons: A lot of hard and difficult work.

Pros: Good work-life fit, decent compensation.

Cons: I was hired as a Machine Learning Engineer, but ended up debugging and optimizing legacy ETL scripts with no data exploration, analysis, or opportunities to formulate business objectives that can be achieved through ML. This is because the Product Managers I was working with had been working on the same product for 15 years. They believe that ML is just a bunch of buzzwords with no potential to help the business. The managers do not like to be challenged to explore new ways of looking at their products' data to find and fix loopholes. They rely on their domain understanding and make decisions based on their "gut feeling" instead of being data-driven. They need to adapt to machine learning thinking by gathering alternate data on their products, forming hypotheses, and coming up with solutions to test them to improve their business, rather than using ML at hackathons just to demonstrate that they hire people who know ML.

Pros: Good environment. Hard job. Learned a lot.

Cons: Bad manager, bad environment, hard job.