As a Data Scientist at Faculty, I’m currently part of one of the teams helping the NHS tackle the COVID-19 pandemic. I couldn’t really ask for a better opportunity to translate my skills and past experience into a positive impact on the world.
Where I am now is exciting, but the story of how I got here might be particularly interesting for anyone looking to start a career in data science.
Why choose a career in data science?
Previously, I worked as a medical doctor in the NHS. I loved medicine, but I also loved other things, like engineering and computer science. At a time when technology is increasingly becoming a vital part of how healthcare works, these interests ended up complementing each other nicely.
As I moved through my medical career, I spent more and more time in technical pursuits related to health. I completed a couple of masters degrees in Medical Engineering and in Medical Imaging, and spent a year in the health tech industry. During that time, I came to realise how much more tangible your impact can be if you are able to bring technical skills and expertise to the table. Suddenly you can do more than imagine and plan ways around difficult problems – you can actually build solutions. All this helped me realise that a career in data science would be a great fit for me and ultimately I decided I wanted to make the transition. But how to make the move?
My Faculty Fellowship project: using data science to predict drug side effects for Birdie
The Faculty Fellowship – a programme is primarily aimed at STEM PhD and masters graduates looking to transition from academia into commercial data science – was a big part of my move into data science.
It’s an eight week programme, made up of two weeks of focused (and pretty intense) training on both the technical and non-technical aspects of data science, followed by a six-week project with a partner company. At the end of your project, you get the chance to present the results of your work to an audience of hundreds of like-minded and interested people, many of whom are leaders in the industry.
During my fellowship project, I worked with Birdie, a company that makes software for care agencies looking after elderly people living at home. The elderly people that Birdie supports often take a wide variety of medications. The problem is that, the more medications you take, the more likely it is that some of them will cause unexpected side effects.
I worked with Birdie to learn how the medications taken by the people under their care were associated with various adverse events like falls or unpleasant symptoms. The technical solution I developed turned into a really powerful tool that could help to predict these problems before they arose.
It felt great to deliver something with such a potentially tangible impact in social care. Altogether, the project helped to reinforce my feelings about the nature of data science work within industry: building real technical solutions to real problems is incredibly rewarding.
How did the Faculty Fellowship shape my career?
There were many things I was able to take away from the fellowship at Faculty. There were obviously opportunities to further develop myself technically, but I also picked up a whole range of useful skills related to the planning, execution and delivery of technical projects. It’s incredibly valuable to learn how to bring a diverse group of stakeholders along on the journey to delivering an impactful data science project.
During all of this, I got lots of help and advice from my mentors at Faculty, as well as the professional network that I gained during the program. This network of data scientists, engineers and commercial specialists is really inspirational and has become a supportive community of connections that I’m sure will be a source of help and advice for many years to come.
I hope this provides some insight into how the Faculty Fellowship can support such a transition. It’s certainly an experience I found to be extremely valuable and something I’d suggest considering if you are looking for a route into the amazing world of data, machine learning and tech.
If you’re interested in making the transition from STEM academia to commercial data science, find out more about the fellowship and submit your application here.