For me it is a red flag in terms of scalability as lots of our data sets won't fit in mongo backed by a 1-2 TB disk even if they take up < 100 GB in the original format (usually binary/compressed genetic data).

It also uses a ton of ram and performance really suffers when the data won't fit in ram so it isn't a great choice if you are trying to push the limits of what your machines can do.

They are only using it to store models and whatever "behavioral data" is but models for things like random forests can be really big and you want to be able to write/read trees from separate machines etc.

I wonder why they chose to use mongo vs local disk or HDFS which they already require.