I'm analytical chemist specialised in spectroscopy and chemometrics.
Chemometrics is statistics for chemical questions/tasks/problems (similar to psychometrics, biometrics, etc.) and definitively a search term your friend should check out.
works on the confluence of statistics and chemistry, of which he could not find many articles online.
This may be because that intersection is a rather specialized field, and thus much smaller, than, say, organic chemistry.
OTOH, it may also be due to not knowing the terminology to search for. Finding such search terms can be very difficult, particularly in these interdisciplinary and applied stats fields: many of them have their own terminology, as people from various disciplines may describe situations which are similar in their statistical aspects with very different terms. E.g. I'm currently looking into aspects of nested designs/data structures. Which I describe to chemists as hierarchical, to data base people as having 1 : n relationships, and which in the social sciences are known as clustered.
A third possible reason is that at least over here in Germany, I'd estimate that far more such activities are going on in industry than in academia. Which may lead to lower priority for publication than implementation.
As @Ingolifs already pointed out, there's a whole lot of statistics in chemical process optimization, and that's related both to (chemical) analysis (PAT: process analytical technology) and synthesis.
The "Applying big data to chemistry and drug design" would be related to QSAR (quantitative structure-activity relationship)
I do not mean the chemical statistics of laboratories, like mean, median, mode of results obtained from experimental data. I mean serious statistics and chemistry.
I suspect that you do have a misconception here. In my experience, many of the simple analytical textbook situations have a habit of needing rather more serious statistics as soon as a little bit of reality happens. The textbook may be fine with a bit of mean and median and a hyperbolic confidence interval for a linear calibration function. With real life analytical data and its more complex structures of influencing factors and confounders as well as experimental constraints that do mess up any simple design for the experiment. E.g. I find myself today first using a mixed model to analyze the data I want to use as reference for calibration of some other measurements later on.
So, applied does not equal easy (or "not serious") - if you take applied data seriously, you'll find that many assumptions made life in "more serious" theory easier do not hold in many application situations.
I'd say that there's a huge amount of serious work still to be done in this area. At least I can say that astonishingly often the answer I get for my "this is the situation, we cannot use the easy case here, because of .... How can we approach this?" is: good question - please tell me once you know - there's no known solution so far.
A colleague from stats/data analysis once complained about "us chemists" that we either have tasks that are no fun because the solution is obvious - or that so hard that they are impossible to solve ;-) (which I guess is pretty much the same for all those applied stats fields)