- Advancement is slow.
- Management talks about horizontal culture but in fact it is very hierarchical, which can be disorienting until you learn to separate lip service from reality.
- Bias towards status quo and conformity. Many employees are primarily concerned with protecting their position and appearances, making honest discussions and appraisals of situations difficult.
- Lots of engineering work for scientists and analysts. More data and ML engineering support would help improve the pace of production and free up scientists and analysts to use their strengths.
- The tech stack is old and the mindset towards developing data science products is very old school. The justification for using outdated and suboptimal approaches is usually a mixture of "we can't do that because we are too lean" and "we don't need to do that because we have been doing fine without it".
- Management doesn't know what they want from Product Managers, leading to high turnover in this role. I would beware of joining as a PM.
- Poor trust and communication between marketing Account Managers and data scientists. AMs can be forced to use algorithms which obviously aren't working well and if you ask the data scientists what is going on, you will get evasive maneuvers and bluster about how "sophisticated" the models are.
- Poor record of retaining female tech talent and exceedingly few women in leadership roles.
- Culture is ultra-WASPy fake nice. Diversity groups are forums to virtue signal, praise the company for how inclusive it is, and avoid challenging discussions that could upset anyone.
