Yes I have the same feeling (for now), that at many times I have to implement things from scratch using Common Lisp, and that pushes me to understand both the essence of the algorithm and how computation works.
As I am still in the early stage of learning Lisp and just switched to CS, I am learning many mind-blowing things at the same time (SBCL+Emacs+Slime), this combination sum up to create a quite steep learning curve, and yes they are just time black holes, I have invested more time than expected every day in them and sometimes have to use Python to get the job done first to make my advisor happy, and then try rewrite in Lisp in leisure time.
Maybe it can be good for me to learn a bit of Lisp every day, because I don't expect to understand all the ideas in the SICP book and remember all rules in the Practical Common Lisp book in 21 days.
As I am doing some research, Python is indeed effective in exploring data/testing preliminary models. But most recently as I just experienced, it gets unbearably slow sometimes. (By unbearably I mean >15 minutes on PC.)
Performance is also important for me, not only for the sake of "big data", but also for self-fulfillment: feels much better if the code gives result in 10 secs rather than several days. (By the way, does CL have mature support in parallelization?)
My lab mates also use Mathematica a lot and it is even more convenient in testing models for data mining tasks, but I have been warned that some packages are buggy at this moment, and most modules are just black boxes that don't allow fine-grained tuning.
I also tried R and at most times don't quite understand the logic of the language. Maybe as you pointed out, it is the gene of the language that determines its shape.
Thanks again for your response, it is good to see someone doing real stuff with Lisp for data mining tasks nowadays.