"Data science" means different things to different people.
You could write a similar statement about having a formal Statistics background or not. It could help, but is not essential.
Substitute CS for "hacking skills" if that's appropriate, in the famous Venn diagram:
You can go a long way without having all three skill sets.
My degree (some 25 years ago) would nowadays be likely called data science or similar, but we did very little computer programming and only did a bit of Minitab (I think). Thankfully there has been progress since that Stone Age, but I would still see the programming and the databases as tools, where the emphasis would be the other way around for the developers and the architects, etc.
I recently had a meeting with the company's Data Architect who pretty much dismissed R as being something which just did some plotting after his Hadoop cluster had done all the work. He reckoned that only summaries like means, etc. were required. On the other hand I had no understanding of how his overall infrastructure hung together (in a very large organisation) and why seemingly straightforward things couldn't get done, so it was difficult to make progress.
Additional knowledge all round may have helped, but there is always the problem of being the jack of all trades and master of none.
Drawing on similar discussions elsewhere R users don't tend to come from CS backgrounds, whereas the current growth in Python looks to be due to CS people entering data science.