Experience: a couple of years as a software developer, before I got bored and return to do a PhD in CS. About to finish my PhD, but I'm already working as a researcher doing machine learning modelling on biological data at one of the research centres at the uni, alongside other data analysis and software development duties.

Typical workday:

First half of the day

Either working on data analysis scripting task (mostly in R, uurrghh) or doing webdev. For data analysis, most of the work here involves getting the data in the right format, e.g. parsing, removing crap, so that it can be loaded nicely as R dataframes. Once we're there, the actual data analysis tend to be fairly straightforward, using t-test, anova, etc (which is just a couple of lines in R). The web dev task involves turning existing data analysis pipeline into something that's a bit more user-friendly for the general use. There's also usually some element of interactive visualisation involved here. For this, we use django on the backend and various javascript libraries on the front-end.

Second half of the day

Working on model development. For me, it's mostly various mixture models with fancy (non-parametric) priors, topic modelling, etc. Haven't had the chance to jump on board of the deep learning craze yet, and probably can't quite justify that until we find a suitable problem and data on-hand. The work here will involve designing new models and inference procedures (so this is math works, on paper), coding out fast inference, model validation, creating synthetic data to test the model, making lots of plots to illustrate the results etc. Primary tools involve using the scientific Python stack, using libraries such as: numpy, scipy, matplotlib, ipython notebook, pandas, seaborn, cython. This is my favourite part of the job, but can really be quite tiring.

Being in a research environment, there's also a lot of flexibility regarding timing. Working from home, whatever, as long as the job is done. My only complain is the rather low pay, compounded by the fact of being in the UK. I think I'll stay here a bit more to build some profile and experience before jumping ship to the industry.