[mhrmsn]

I work in data science for a pharma/chemical company. Broadly speaking, our team is applying machine learning to chemistry and biology-related problems. Those are usually falsifiable and can and will be validated through lab experiments.

The main problem here is that experiments tend to be expensive - depending on the problem a single data point can easily cost from $100+ (sample preparation and measurements) to $100k+ (e.g. synthesis of a new compound). So our datasets are often small, and there is some barrier for lab colleagues to trust/try out some new ML model vs their status quo.

But it is quite rewarding when it works and one also gets to interact with people from different disciplines on all sorts of interesting problems :)