Stay alert and adapt! Data science and AI in drug discovery

Andrea Gondova tells how her MSc in Bioinformatics from Edinburgh led her to join a graduate scheme that is flexible and rewarding.


Over the past few years, artificial intelligence (AI) has grown from an obscure specialism into one of the hottest tech trends of the century across all industries. The pharmaceutical sector is no different with numerous pharmaceutical companies investing large sums in collecting and curating ‘AI-ready’ datasets and supporting research in the area.

My relationship with AI in pharma started during summer 2018 while working towards my MSc in Bioinformatics at the University of Edinburgh. Focusing on the application of deep learning to prediction of activity of drug-like molecules in my dissertation, I became intrigued by the drug discovery process. It is notoriously difficult, long, and costly, presenting many opportunities for the application of AI at all stages with examples ranging from the discovery of new drug targets, through molecular design to drug response prediction, medical imaging and optimisation of clinical trials.

It was around this time that I received an email alerting me to AstraZeneca (AZ)’s Data Science & AI graduate programme. Consisting of 3 placements, 8 months each, the programme allows you to move across the company. The focus of the placements is very much up to the graduate. I, for example, started last December, working on analysis of 3D magnetic resonance images and then moved to developing a recommender system for diagnostic tests. Not many jobs are this flexible (or fun)!

As expected, placements can involve busy days and staring at the computer screen for long hours. Skill requirements can vary significantly with the project, and selection of the programming language/methods mostly depends on the graduates and the teams they work with. In data science, where new approaches spring up like mushrooms, it is important to stay alert, adapt to demands and learn quickly as the necessity arises.

The most surprising thing about working at AZ was simply the number of discussions, meetings and presentations it involves. Communication is an integral part of the job. This can be a little daunting at first but also a great confidence boost. To be listened to by experts in the field really makes you feel like a valued part of the team, that your work matters. The working environment here is just wonderful. How much this is AZ-specific I cannot say, but if people see the motivation and efforts to improve, everybody, even the senior colleagues, are very supportive and happy to give advice and help when necessary.

The company was looking for graduates with a STEM Bachelors or Masters degree. Most of the recruits have an MSc.

I often wonder what made my application successful. I did not really have a clear career path in my head or, let’s be honest, much work experience within the area. What I did have was scientific curiosity and the desire to create something useful, potentially life-changing; also a strong motivation to learn about the industry and a genuine interest in the research. If these don’t come across as genuine in your application, first, assessors know, and second, the job is probably not the best fit for you anyway. Thinking back, I personally could not have made a better move after my MSc. But be honest with yourself, there are so many opportunities out there, especially for data scientists, is this the one for you? It’s not an easy question to answer. Do your research, ask, learn about the job and the company.

I hope that sharing my experience will help you when you make a decision about your next steps. Good luck.



(Image - Andrea Gondova)

(Image - Andrea Gondova)


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