I would say that the answer is trivially yes. You need to know math to do great science.
Why?
Science is all about reproducibility. Without a strong grasp of statistics, you do not know whether you've actually discovered an effect, or whether you just got (un)lucky with your data. And far from being a theoretical problem, numerous scientific disciplines, especially those in biology and medicine, are currently dealing with a "reproducibility crisis", as they find that prominent results don't recur when replication experiments are run.
Far from there being too much math in science, I think there's currently too little math. If researchers knew more about the limits of the statistical tests they were using, rather than focusing on the magic p=0.05 level, we'd have much higher quality science, with a much higher level of confidence that the results being reported were real results, rather than statistical abberations.
But (1) statistics is really not the kind of math that is being talked about in the article, (2) even if you study statistics in a graduate program, you'll be inundated with hypothesis tests and alpha=0.05 and any deeper insight just kind of has to develop on its own, (3) the only math you need to understand the shortcomings of statistical tests is P(A|B) != P(B|A) and (4) the reproducibility crisis is primarily due to publication bias and null hypothesis significance testing is at most a catalyst.
Not only that but the purpose of science is also to make quantifiable predictions.
If you treat math as a blackbox the best case scientific contribution you can make will be reduced to measuring numbers. New numbers might lead to some new major insight, however this new insight will most certainly not be delivered by you but by someone who uses your numbers to actually "do the math" hence develops a mathematical theory capable of delivering quantifiable predictions for a more general case including the new data.
Can this really be said in all fairness? What of the flashes of brilliance, the striking insights into understanding seemingly inexplicable phenomena? The discovery of the structure of benzene, the creation of the periodic table comes to mind.
Why?
Science is all about reproducibility. Without a strong grasp of statistics, you do not know whether you've actually discovered an effect, or whether you just got (un)lucky with your data. And far from being a theoretical problem, numerous scientific disciplines, especially those in biology and medicine, are currently dealing with a "reproducibility crisis", as they find that prominent results don't recur when replication experiments are run.
Far from there being too much math in science, I think there's currently too little math. If researchers knew more about the limits of the statistical tests they were using, rather than focusing on the magic p=0.05 level, we'd have much higher quality science, with a much higher level of confidence that the results being reported were real results, rather than statistical abberations.