Null values as useful information for feature engineering
It would depend on the type of model you want to train.
If you are doing a classification model ( Yes/No, 0/1 type of class) then I suggest you use dummy variable generation functions in python. 

I would also recommend that you convert all your other categorical variables into dummy variables in order for your model to take every bit of information into account.

one popular technique in feature engineering is one-hot-encoding to change the nature of your data.

So generally you would create a new variables ( as you have mentionned) that has 2 values :


1 if the variable is NULL
0 if the variable has a non-Null value
Below are some links with nice examples:


https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html

https://jamesrledoux.com/code/dummies

Good luck !