Pros
The teams were collaborative, supportive and full of hardworking people. The projects were interesting and there were opportunities to learn a lot if you were self motivated and willing to take initiative.
Cons
Some managers within the data science and ML teams were very poor at technical and people management, which created challenges for engineering teams. Engineers often had to take on additional responsibility for technical direction and execution because management was not always able to provide enough technical guidance, support, or clear direction. There were also concerns around bias and inconsistent decision making related to recognition, ownership opportunities, visibility and overall career growth. At times, engineers felt that recognition was not always fairly attributed, with management often receiving more visibility for work primarily executed by engineering teams. Mentorship and career growth support felt limited and development expectations were not always clearly communicated.