Filling missing values for important features
I would first build some models without those predictors. If 2/3 of your training set is missing so you can't have your basic model based on them. If your accuracy is too low I would try to use a smaller data set and not use the missing values. Those kind of missing values are not somthing that you can easily define (like a default job) unless you have a prior on your data (e.g most people in the data are data scientists). If you do have any kind of such prior you can use missing data methods more efficiently.