Transitioning from Technology to Biotechnology: Benefits, Challenges and Expert Advice

AI and ML Jobs

Photo: Professional working on two computers/Ar

Photo: Professional working with two computers/Arsenii Palivoda/iStock

More than 168,000 workers have been laid off in the tech industry since early 2023, many of whom may be looking to transfer their skills to the life sciences.

As biopharmaceutical companies increasingly embrace artificial intelligence (AI) and machine learning (ML), they are actively hiring talent to fill these technology-focused roles.Businesses are slowing hiring due to the economic climate, but since January 1st, the number of tech job openings has been bio spacee job boards are declining at a much slower pace than job vacancies in other fields.

said Leah McGuire, technology lead for automation and analytics at Benchling, a cloud-based platform for biotech R&D. bio space She has seen firsthand the changing demand for biopharmaceutical technical professionals.

McGuire started his career with a desire to be a data scientist in a biotech company. However, I quickly realized my options were limited because I didn’t have the highly specialized qualifications that biotechnology demands. Instead, she got a job as a data her scientist at her LinkedIn.

“Organizations were looking for really specific and specialized skill sets,” McGuire said in an email. For example, she said it’s common for companies to look for candidates with PhDs in specific fields such as drug discovery. “Working in technology that didn’t require me to immediately follow a highly specialized route was more appealing,” she added.

McGuire has found his way back into life sciences and will begin his role at Benchling in 2021. After she returned to her industry, her companies seemed far more interested in candidates from a wider range of backgrounds than when she started her career in 2012, she said. she said. .

“I think this is really beneficial for the industry…” she said. “By involving people with diverse technical backgrounds, we can cross-pollinate ideas and arrive at more comprehensive solutions.”

Transition to biotechnology

There are ample opportunities for engineers in the biopharmaceutical field. However, the transition can be difficult, especially for those who have never worked in a scientific environment.

One of those challenges is a lack of resources, McGuire says. Technical professionals are only a small part of a biopharmaceutical company, and large technology companies may not have the same tools and software systems that help streamline common tasks.

“Believe it or not, many biotech researchers still rely on paper, email, spreadsheets and consumer collaboration tools because of the complexity of their work,” McGuire said. I’m here. “There are very few tools made for them.”

Some large companies are investing in building custom software to solve this problem. Others have to rely on what she called “legacy software,” built for vastly different times of both technological and scientific innovation.

Natasha Seelam, Senior Deep Learning Engineer at Sherlock Biosciences, also quit her job at a technology company to take up her current role.she said bio space One of the most difficult aspects of working in biotechnology is the difference in data sets.

Data are even scarcer in biotechnology. For example, if a tech company primarily deals with language models, algorithms can scour the internet for all sorts of language-related data, she said. Life science data collection is more difficult. All data must come from experiments and tests, which are often expensive and time consuming.

The unforgiving nature of clinical trials, Seelam said, is another big difference he’s noticed between technology and biotechnology. Technology, she said, has more room for error than life science.

“If an experiment goes wrong, a single failure can cost hundreds to thousands of dollars,” she said. “So you have to be very smart about how you use your work.”

Even after a trial or experiment is over and the data is collected, it can still be difficult to work with because of the scientists’ own biases, Seelam said.

“There’s an inherent bias in that data because people don’t want to fail. They want to ensure that some of these experiments will succeed.”

lesson learned

Despite these challenges, Seelam said he does not regret moving to biotechnology. Still, there are some lessons of hers that she wished she had learned sooner.

One of them is how to accept rejection.

“Looking at all these people with these blazing records, they’re so unbelievable.” No one will tell you.”

McGuire advised industry newcomers to take the time to understand the problem and task at hand.

“Until you have a nuanced understanding of the problem you’re trying to solve, it’s hard to tell if you’re reaching a misleading conclusion,” she said. It’s about being really interested in and investing in.”

For those who want to make the leap, there is no better time than now, she said.

“Biotechnology is experiencing a data explosion with next-generation instruments, robotic automation, and new scientific methods,” McGuire said. “We are just scratching the surface when it comes to the application of AI and ML in biotechnology.”

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