Biotech Industry Leaders Give Advice to Earlier-Career Scientists
June 9, 2022 | Over the past two years, Stan Gloss, founding partner at BioTeam, has been sitting down with leaders in pharma and biotech for our Trends from the Trenches column and—more recently—the Trends from the Trenches podcast. They have been wide-ranging conversations covering everything from digital transformation at big pharma to All of Us and population genomics, data-driven cancer genomics to AI/ML models in drug discovery.
Along the way, Gloss has asked his subjects to also weigh in on the life sciences’ career transformation—how did they arrive at their current roles and what advice would they give earlier-career scientists. Here’s a collection of some of their answers and advice.
Lihua Yu, Fog Pharma
I think the longest, the lasting career impact is actually when your own capability, your own vision, your own inner strength are matched with the responsibility you have. So the ambition shouldn't be in the beginning to say, "I have to be a CEO of a company," or whatever that title. The ambition should be always looking for self-growth. Making sure you match yourself to the next opportunity.
Anxiety doesn't come from, “Why am I not being promoted?” Instead your focus should be: "Am I pushing myself to the next level, so I will be ready to take on the next opportunity? Am I growing myself?”
(Read the full article: H3’s Data Centric Approach to Cancer Genomics)
Glenn Lockwood, Lawrence Berkeley National Laboratory
Ten years ago, I was at graduate student in material science. I am very, very grateful that I have followed a path that's pretty much better than I could have ever imagined. Ten years ago I knew I wasn't particularly happy doing what I was doing, being a material scientist, but I would say, just keep doing what you're doing. It's worth it. There is a future out there. Just because you're doing research now and it doesn't seem like it's relevant to making the world a better place, the skills that you learn even if they're not directly relevant to your interest in what you want to do with the rest of your life. Those are invaluable skills to have, being able to think in a logical way, construct ideas and propose thoughts and work with other people. The context of those things is not as important as the act of learning how to think critically about things and build up arguments and just acknowledge information.
(Read the full article: Five-Year Plan: The Changing Landscape of Science, Storage)
Kjiersten Fagnan, Department of Energy, Joint Genome Institute
I came into this work with this high-performance computing background where we really like to think about performance and scale and optimization. I would like to sit myself down early and explain how dynamic biology is, and how much things are changing, and how much our understanding has the potential to change on these really different time scales.
Understanding biology gives you a different appreciation for what's needed, both from the hardware and software infrastructure, but also how to support and empower these communities. I probably would have started advocating earlier for some of the software infrastructure that we're trying to build now to address the fact that everything is moving into this more distributed framing.
Yes, it's going to be very hard to find partial differential equations and the science that JGI is doing and maybe eventually we will get to where we have a more of a mechanistic or first principles understanding of what's going on in biology. But there really isn't a single deterministic answer to a lot of the work that people are doing because you're studying and perturbing living systems.
These answers really do look like distributions and I would have prodded myself to continue thinking harder about statistics which is maybe not one of my favorite subjects, but it's of critical importance when you're trying to understand something with so much variability.
(Read the full article: The Role of Data Management in Advancing Biology)
Saira Kazmi, CVS Health
Whenever I’ve hit a roadblock or a plateau, I dig deeper to understand what drives me and push towards my passions, which in my case has been engineering. Don’t be too fixated on what you think a typical career path should be.
Early on, try different things to explore and find what drives you. I realized that softer skills are not emphasized in the STEM curriculum and were an area of personal growth. Focus on diversity of experience. Try learning a new language, playing music, playing a sport, creating art, and participating in performing arts. Some things will stick with you. You’ll never fail once you find your passion because you will work hard naturally.
(Read the full article: How to Manage Data as an Asset: Opinions from a Fortune 5 Executive)
Mike Montello, GSK
The best advice I would give is to never be content in your current position and always maintain a north star to what you want to achieve five years from today.
There was one time in my career where I became a bit content, and there was a restructuring at the company where I worked which changed my role. The restructure lit a fire for me to get moving to update my skills. The restructure also forced me to go out and see what other roles were available. Looking back, I remembered I treated the situation in a positive light: as an opportunity. It was an opportunity to develop and do something new. After this situation, which happened early in my career, I adopted a growth mindset and now continuously challenge myself to learn and grow versus wait for a trigger outside of my control.
The pace of adoption of tech is at a level I haven't seen before in the biopharmaceutical industry. Digital adoption has been accelerated as we managed continuity through the pandemic and launched solutions for COVID-19. It’s important to not sit idle and to keep learning. Keeping up is hard but we must becoming comfortable that skillsets today may be obsolete in 3-5 years.
(Read the full article: GSK’s Digital Transformation Roadmap)
Lita Sands, Life Sciences, Amazon Web Services
When I got my undergrad degree, I mentioned to you that I was very passionate about technology. I didn’t just major in Computer Science, I also majored in Business with a Marketing emphasis. I always felt, intuitively, that one needed the other to really shine. What I would tell folks today is if you have a technical background, find a way to apply it in this industry. If you have a science background, make sure you’re up-to-date on the technology and follow your passion. My favorite person right now, who I look at as a hero, is Christoph Gorgulla. He is a postdoctoral researcher at Harvard University. He came with a passion for trying to target the most difficult areas of discovery: protein-protein interactions. These are considered undruggable targets. This is very, very difficult. He took his passion for science and his passion for technology, and he created a platform called VirtualFlow. This is a structure-based virtual screening platform that will enable rapid analysis of protein-protein interactions. Normally, if you worked in a wet lab, you would be doing normal high-throughput screening, can at most process 500,000 to a million compounds, and that would take weeks or months and be very, very, expensive. With VirtualFlow, he was able to process billions of compounds in 6.5 hours and set a record for AWS for lighting up the most CPUs in any region. That was 2.3 million virtual CPUs, and this is mind-blowing. The cost was $140,000 to do this. To me, he exemplifies the possibility that’s out there today that the opportunity for the cloud has opened up to so many researchers. It’s just amazing. I think there is still so much more room for innovation in our industry no matter what level you’re in at an organization or what walk of life you come from. I’m really looking forward to the next ten years.
(Listen to the full conversation: Lita Sands on the Cloud, Digital Transformation, AWS Future Plans)
Kim Branson, AI/ML, GSK
You need to think very mindfully about your career rather than just being led by the whims of chance or, “I’ll just take a job here.” Think about, “What skills do I need to accumulate? What experiences do I want to have?” You have to think very carefully about which jobs you take, where you take them, what you’ll learn from them, and how it helps build that journey.
Software engineering is fundamental. You have to write good code. You have to know these things so you can stand on a good foundation. You have to keep up with it. That’s really important. You have to realize that not everything is a deep-learning problem. There are plenty of problems for which classical machine learning methods if there is such a thing, will get you there. You have to think of the needs of what you’re building. What is the design criterion when you start building something, and what does it have to be? Experience a lot of different domains and realize that going from zero to something is better from zero to trying to build the biggest thing. Really understand the process and the people who generate the data. Work with them and understand what their problems are: communication. Explain how things work and the limitations. That’s really important because it lets you understand how the data is generated. Don’t just take data that’s sort of thrown all over the world. Communication is really key. Once you build a model, the model changes the world around it. You have to understand when to re-train and monitor these things in production. You can build the best model but if people don’t know how to use it, understand the results, or know the way it works, it’s not going to have the intended effect. You’ve got to get good at really talking to people, understanding their questions, and really demystifying what you do. Explain that it’s not magic; it’s mathematics. It has limitations. Don’t oversell things. Get really good at software engineering because that’s really going to help you build things. At some point, you’ll move on, and someone else is going to pick it up. It might be future you. Be kind to future you or someone else, and write great and well-documented code. These things have a way of sticking around for a long time. Be very thoughtful about where you go to work, who you’re working with, and who you’re learning from. Don’t maximize income over ethics. You need to think very carefully about the ethics of the company and the behaviors that you’re joining. If it's a company that has historical problems, it means you need to look at the people around them. Sometimes, these companies don’t change, so you need to be very careful about that. If you build these things and they get used, just think about how they’ll get used in society. That’s a debate that a lot of people are now realizing. That’s why I work in healthcare. Just be careful about where you go and who you work with. It’s absolutely key.
(Listen to the full conversation: Kim Branson on Building Groundbreaking AI/ML Models in Drug Discovery)