Bastian works with biological data to improve patients’ lives

Bastian Schiffthaler left academia for safer employment conditions and the possibility to more directly improve patients' lives.

Image: Björn Schiffthaler

The decision to move from academia to industry was not a sudden one. For Bastian, it developed gradually over time. One important factor was the long-term uncertainty that often comes with an academic career, with continuous pressure to secure funding and publish results. Another strong motivation was the possibility of working closer to real-world impact.

“In the pharmaceutical industry, what I do is more directly improving people’s lives,” he explains. “Even though it takes many years to develop a drug, I know that our work can directly help patients. That is very rewarding.”

Although his doctoral research focused on plant science, biomedical research had always been in the back of his mind. 

“I originally wanted to study medicine, but after working as a paramedic for a year I realized that I was not a patient-facing person,” he says. “Still, contributing to medicine in some way was always something I considered.”

PhD training as preparation for industry

Looking back, Bastian sees his PhD training at Umeå University and the Umeå Plant Science Centre (UPSC) as excellent preparation for his current role. Working with trees and other non-model organisms meant that standard methods often did not exist, forcing him to think creatively and develop new approaches. 

“During my PhD, nothing worked out of the box,” he says. “I constantly had to solve new problems, whether they were experimental or computational. That kind of problem-solving is exactly what I do now.”

My supervisors gave me a lot of freedom, which was great. But it also forced me to learn how to manage my time, prioritize, and take responsibility. 

Another important part of his PhD experience was the level of independence he was given.  

“My supervisors gave me a lot of freedom, which was great,” he says. “But it also forced me to learn how to manage my time, prioritize, and take responsibility. These self-management skills are extremely valuable in industry.”

Working with biological data in interdisciplinary teams

Today, Bastian works in data sciences and quantitative biology at AstraZeneca. His work involves analysing complex biological datasets, developing new computational methods, and collaborating with large, interdisciplinary teams. 

“Compared to academia, there are a lot more meetings,” he says. “Ideally, we are involved before a project starts, giving input on experimental design and potential pitfalls. We develop methods, perform hands-on data analysis, and stay involved as projects evolve.”

“In academia, I was mainly responsible for my own work. Now, my work affects dozens of other people. That comes with more pressure and less room for curiosity-driven exploration.”

From complex data to patient impact

One of the most rewarding aspects of his work is knowing that the research may eventually improve quality of life for people with serious and chronic diseases. At the same time, the scientific challenges are substantial.

“The most challenging part is the complexity,” he says. “The low-hanging fruit is gone. Projects are now multimodal and involve many interacting factors. We have to consider safety, targetability, and downstream effects, which is why these projects often require 50 or more people working in parallel.”

For current students considering a career in industry, Bastian emphasises the importance of developing strong data and computational skills. He also encourages students to build a visible portfolio of work, for example by contributing to public code repositories or analysing open datasets.

“Public code, projects, anything that shows what you can actually do makes a big difference,” he says. “Publications are important, but it’s often difficult to see what someone contributed unless the code is available.”

Human expertise is needed in an AI-driven field

Looking ahead, Bastian believes that bioinformaticians will continue to play a crucial role in life science research, even as artificial intelligence becomes more widely used. 

“Artificial intelligence can already do many things that junior scientists used to do” he says. “But it is still not good at discovering new biology or understanding complex experimental limitations.”

“The cutting edge - finding new pathways, developing new methods, understanding confounding factors - still requires human expertise,” he adds. “Bioinformaticians will be needed to understand the data deeply, recognises its limitations, and design analyses that are scientifically sound.”