Scaling does not speed up the SVM model
I do not program on python. Nevertheless, I would say the key relies in the number of samples (629,145). They are many. The SVM has to test them to pick which are good support vectors for data partition/regression and given the size of the dataset there are a lot of alternatives. That issue plus the number of different C, gamma/sigma and, perhaps, epsilon (not sure you are classifying or regressing) tested during the optimisation of the SVM hinders convergence.
There are people who used clusters instead of the original dataset to train the SVM. For instance: 
Barros de Almeida, M., de Padua Braga, A., Braga, J.P., 2000. SVM-KM: speeding SVMs learning with a priori cluster selection and k-means, in: Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks. Rio de Janeiro, (Brazil), pp. 162–167.
can be a good starting reference but, I am aware there a others. There is at least one R package based on that idea (LinearizedSVR).
In addition, there are analytical methods to infer the values of the parameters C, gamma/sigma and epsilon (for regression) based on the characteristics of the training dataset. That means no optimisation is necessary, although there is a friend that still tunes gamma/sigma but fixes C fallowing these approaches. I think there is available code (at least for R) somewhere in the net. The refs are:
Cherkassky, V., Ma, Y., 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (1), 113–126. 10.1016/S0893-6080(03)00169-2
for regression and, 
Keerthi, S.S., Lin, C.-J., 2003. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Comput. 15 (7), 1667–1689. 10.1162/089976603321891855
for classification SVMs.