Artificial intelligence has been part of drug discovery for years, but companies are now using it in ways that are starting to show up much more clearly in hiring. For biotech and pharmaceutical companies, the question is becoming less about whether they need AI expertise and more about what kind of expertise will actually work alongside their scientists.
Some of the most interesting work in drug discovery is happening where computational science and traditional biology come together. Researchers are using machine learning to help evaluate compounds, sort through large biological datasets, predict molecular behavior and decide which experiments may be worth trying in the lab. That shift is changing the types of candidates companies need to hire and how those candidates are evaluated.
A strong data scientist may have impressive technical experience and still have a hard time in a drug discovery environment without enough understanding of the underlying biology or chemistry. The opposite can also be true. A highly accomplished scientist may understand a disease area inside and out but have little experience working with computational models or the types of datasets these systems depend on.
Because of that, companies are looking more and more for people who can move between those two worlds. The challenge is finding candidates who not only have strong technical or scientific skills, but who can also communicate across disciplines and understand how their work fits into the larger drug development process.
For hiring managers, this makes evaluating candidates a little more complicated. A resume filled with AI terminology doesn’t necessarily tell someone whether that candidate can contribute to a discovery program. Hiring teams need to understand what the person actually worked on, what type of scientific data they used and how close their work was to an actual drug program.
Those details can tell a hiring manager more than a list of programming languages or AI tools. A candidate may have built models, interpreted results, designed experiments around those findings or worked closely with laboratory scientists who tested their predictions, and each of those experiences can mean something different in a discovery setting.
The same idea applies to traditional scientific candidates. Companies don’t necessarily need every biologist or chemist to become a machine learning expert, but they may place more value on scientists who are comfortable working with computational teams, questioning model outputs and using those results as one part of a larger scientific decision.
This also means job descriptions may need another look. Asking for deep experience in biology, chemistry, machine learning, software development and drug development in one person can quickly turn a search into a hunt for someone who may not really exist. In many cases, it makes more sense to decide which capabilities truly need to sit within one role and which can come from the surrounding team.
There is another part of the hiring picture that is easy to overlook. More AI investment doesn’t automatically mean every company will build a large internal AI department. Some organizations may choose to keep smaller internal teams while using outside partnerships for certain capabilities, which can make the positions they do hire for more specialized.
The technology itself is still changing, which makes long-term hiring plans difficult to predict. Researchers are already experimenting with AI systems that can pull information from different data sources, propose solutions to biological questions and assist with molecule design. As those tools improve, the skills companies value today may continue to shift.
For life sciences companies, that makes scientific judgment especially important during hiring. The best candidate isn’t automatically the person who knows the newest AI platform. It may be the person who understands the science well enough to know when the technology is useful, when the results need to be questioned and what should happen next in the lab.
Recruiting for these positions takes the same kind of judgment. As drug discovery roles become harder to place neatly into traditional scientific or technology categories, companies need to look beyond job titles and understand what a candidate has actually done. For Scientific Search, that means paying close attention to the combination of scientific background, technical experience, and real drug development exposure behind the resume.
AI is changing how drug discovery teams work, and it is also changing what companies need from the people doing that work. The hiring process now has to account for both sides, finding candidates who can understand the science while also working effectively with the technology supporting it.
