Hypertune xgboost to dealing with imbalanced dataset
Given an imbalanced dataset and focusing on increasing your true positive rate, it is quite relevant to use the right evaluation metric (the one used to validate the model being trained on each evaluation round).
In this case, I recommend you use Precision-Recall AUC instead of ROC AUC, so you force your model to focus on the minority class.
A nice post about it can be found here
Another points to take into account could be:

increase the range of possible number of tree estimators in your hyperparameter tuning process
set the scale_pos_weight at about your (majority class samples number)/(minority class samples number)