QA with Riley Doyle, CEO and Technical Lead at Desktop Genetics - Viewpoint - careers advice blog Viewpoint careers advice blog

QA with Riley Doyle, CEO and Technical Lead at Desktop Genetics - Viewpoint - careers advice blog In a recent Viewpoint blog, we looked at the topic of Big Data and how it will impact on life science talent demand. In this QA blog, I talk to an individual who is on the front line of this revolution, Riley Doyle, CEO and Technical Lead at Desktop Genetics, a rapidly growing biotechnology software company based in London. Riley originally studied biochemistry and then chose to switch to software engineering. He worked at Genentech and within the clinical regulatory space on IT management tools before founding Desktop Genetics with two of his colleagues from Cambridge. With his multidisciplinary background and experience, Riley offers unique insights into the advent of Big Data and its impact on recruitment, training and broader issues in the biotechnology industry. What is Big Data and how does it apply to the life sciences? Biology is fundamentally a data science â€" an enormous data science. So much of what you do in the laboratory is experimenting over and over, producing lots of data to come up with some kind of model or conclusion. For example, in my area of genome editing, there are huge amounts of data produced at every step in a given process, and data science helps a scientist apply that data in the laboratory. What we’re seeing now is the emergence of an industry to help research scientists to work smarter â€" the application of data science to biological research. Do you see this sector growing? From our perspective, certainly. Last year scientists used our DeskGen platform to design an unprecedented number of short DNA sequences, or CRISPR vectors, and the number of publications in the field is growing exponentially. The challenges we face are enormous and there’s a huge amount of collaboration going on between biotech, ‘big pharma’ and universities, especially when it comes to making sense of complex data. Symbiosis is all-important in this field â€" it really does require the combined effort of thousands or even millions of minds. What you’re looking at now is the emergence of a biotech ‘ecosystem’ where companies collaborate in putting together different pieces of the jigsaw. Do you find it easy to recruit candidates with both biological science and data science skills? Finding the right candidates is definitely a challenge. The war for talent is real. In fact, it has become so much of a challenge that we’re considering creating our own formal learning and development programmes. We already do an element of this on an informal basis but we’re going to have to ramp it up. That’s because you tend to find people with a great life science CV or data science CV, but rarely both. What we really need are ‘bio-coders’ â€" people with both sets of skills. If there’s any one common denominator we look for, it’s people who can learn quickly, because no-one comes to us with genome editing as a core skill on their CV! What’s causing the skills gap? The problem is they teach you in biology school what a protein is and how glycolysis works, but they don’t teach biologists how to manage large quantities of data. I started out as a biochemist and then switched over to engineering. It was mind-blowing to discover how software engineers go about handling large amounts of data â€" we’d been doing everything in Excel in the biochemistry lab! It might work for small amounts of data, but not when you’re talking about the equivalent of a DVD box set of data per cell line we require. Do you see this situation improving? Something we’re beginning to see with the younger companies we work with is key people on the technical and operations team coming from a computer science and electrical engineering rather than a life sciences background. It’s an interesting trend and it’s going to increase. If you imagine a future in which each patient in the healthcare system has a terabyte or more of data that needs to follow them, you’re going to need a completely different kind of scientist to work in such an environment. Some universities are taking steps to address the problem â€" in Cambridge in the UK, we see MPhil courses being introduced that go part of the way. Moving forward generally, working with large data sets is going to become a basic life skill for life science candidates. To view our current vacancies for life scientists, please visit our life sciences jobs index. Stay up to date with latest news from across the globe by joining our LinkedIn group, Life Sciences Industry Insights with Hays and follow us on Twitter @HaysLifeScience.