My experience with bioinformatics is almost exclusively on the industry side, and more the informatics than the bio. With that caveat, a few thoughts:
should I prioritize developing skills that will make me more employable and E2G (e.g. develop and apply sexy, ad hoc methods to rich-person illnesses in a more mainstream bioinformatics-y role)
My experience is that the highest earning positions are not âsexyâ (in the way I think you are using the term). I recall one conference I attended in which the speaker was describing some advanced predictive algorithm, and a doctor in the back raised their hand and said âthis is all nice but I canât even generate a list of my diabetic patients so could you start with that please?â
This might also address your question âhow easy is it to, say, break into industry data science for anthropology graduates with experience in computational stats methods development?â â I think it depends very much on what you mean by âdata scienceâ. A lot of the most successful bioinformatics companiesâ products are quite mundane by academic standards: alerting clinicians to well-known drug-drug interactions, identifying patients based on well validated reference ranges for lab tests, etc. My impression is that getting a position at one of these places is approximately similar to getting any other programming job. If you are looking for something more academic though, the requirements are different.
focus more on greater blights afflicting larger numbers of human and non-human animals (say, to understand differential responses to tropical diseases, or maybe variation in the human aging process, or pivot to food science and work on cultured meat or something, as well as work on more interpretable methods)
A problem I suspect you will run into is that methods development requires (often quite large) data sets. I get the sense from your brief bio that you arenât interested in doing any wet lab work, meaning that if you were to work on, say, cultured meat, you would need a data set from some collaborator.
If I were you, I might try to resolve this first. I know GFI has an academic network you can join and you could message people there about the existence of data sets.
Also, you might be interested in OpenPhilâs early career GCBR funding. Even if you donât need funding, they might be able to connect you with useful collaborators.
My experience with bioinformatics is almost exclusively on the industry side, and more the informatics than the bio. With that caveat, a few thoughts:
My experience is that the highest earning positions are not âsexyâ (in the way I think you are using the term). I recall one conference I attended in which the speaker was describing some advanced predictive algorithm, and a doctor in the back raised their hand and said âthis is all nice but I canât even generate a list of my diabetic patients so could you start with that please?â
This might also address your question âhow easy is it to, say, break into industry data science for anthropology graduates with experience in computational stats methods development?â â I think it depends very much on what you mean by âdata scienceâ. A lot of the most successful bioinformatics companiesâ products are quite mundane by academic standards: alerting clinicians to well-known drug-drug interactions, identifying patients based on well validated reference ranges for lab tests, etc. My impression is that getting a position at one of these places is approximately similar to getting any other programming job. If you are looking for something more academic though, the requirements are different.
A problem I suspect you will run into is that methods development requires (often quite large) data sets. I get the sense from your brief bio that you arenât interested in doing any wet lab work, meaning that if you were to work on, say, cultured meat, you would need a data set from some collaborator.
If I were you, I might try to resolve this first. I know GFI has an academic network you can join and you could message people there about the existence of data sets.
Also, you might be interested in OpenPhilâs early career GCBR funding. Even if you donât need funding, they might be able to connect you with useful collaborators.