There's quite a bit of data-anlytics type stuff in quantitative poli-sci these days. Now whether it's good science or not varies, much like in the rest of "big data", where there's no guarantee that either the data or the analysis is always good. There's some very careful stuff, and a lot of data-dredging on datasets of convenience (or over-extrapolation from limited data sets).

More traditional poli-sci is scientific in the sense of the social sciences, which has a fairly long history of epistemological debate I'm only vaguely familiar with. I think I would probably call it scientific in a certain sense, but maybe a different word is needed. I'd group it vaguely with disciplines like anthropology, archaeology, and linguistics as areas with quite a bit of methodological diversity, but still a more empirical orientation than you find in the humanities. Part of the issue is that there is data, but how to interpret the data is complex ("there is no such thing as raw data"). Though for mostly institutional reasons there are some people who are more philosophers or historians who also happen to be in poli-sci departments.

At my school, it depended heavily on what your focus was. We had three professors, each focusing on their own area of specialization: American politics, international politics, and theory.

The American politics professor had an academic pedigree and insisted that Political Science could and should be a hard science backed up by facts and numbers. In his papers you need statistics, studies, and graphs. He focused very much on the methodology of your research and how well you could back up your claims with solid evidence.

The theoretical professor appreciated hard facts but it was much more important to him that you had a well-reasoned argument, and as _delirium said, would much rather read a 10-page paper full of epistemological debate than one twice that filled with detailed Bayesian analysis.

The international politics professor didn't care because he had tenure.