I've seen Python (and R) used all the time for exploratory ML. Do all of your feature extraction, feature selection, parameter tweaking, and backtesting in Python, and then once you have a model that works reasonably well, port the feature extraction for only the features that actually work well over to a compiled language like Java or C++, train your models on lots of data, and do your actual classification in the compiled language.

Most ML is an iterative process, and the final model that's used in production is just the tip of the iceberg of the development work that went on. Python works as well for exploratory programming there as it does for any other domain.