Transition from academia to data science: KCL alumnus Jason’s story

Jason Myers is a Data Scientist at Faculty and he did his PhD in Mathematics at King’s. Today we are excited to talk to him about his career journey and how he made the jump to data science from the world of academia.

Image of computer cables
Photo by Randall Bruder on Unsplash

What are you doing now? 

I’m currently working as a Data Scientist for Faculty, an applied AI company that works with organisations like the Home Office, easyJet, NHSX and ARUP to help them take advantage of some of the latest developments in machine learning. We also run a fellowship that helps STEM PhD students become data scientists through industry placements, in-house training and mentoring.


How did you get there? What was your university and career journey like?

I originally obtained my BSc and MSc in Theoretical Physics in South Africa at the University of Cape Town. I arrived in the UK in 2015 to start at CANES, a centre for doctoral training based at KCL. After working for a small London start-up as a data science intern in the last year of my Maths PhD, I joined Faculty.


What’s daily life like in your role? What drew you to this profession?  

Daily life can take on many forms, although most days I will spend time coding for the development and implementation of our models, helping to figure out where AI can help a client, coding up machine learning models or mentoring new data scientists through our fellowship.

I enjoyed my studies but, through the PhD realised that the academic path wasn’t the right fit for me. So I spent some time trying to find out how I could use maths to solve interesting problems outside of academia. I decided that data science might be the answer and I got a place at the Faculty Fellowship where I became confident that this was the right path (it’s also pretty cool telling your family that you can ‘do machine learning’ – at least it makes my parents happy).


Interested in careers in technology? Come along to our Focus on Technology career festival, happening right now in a virtual environment! We are hosting an AI, Data Science & Machine Learning panel event on Thursday evening at 6pm with industry professionals as guests who are excited to tell students about the diverse and varied careers in technology. 


What do you know now that you’d wish you knew when you were a student? What has surprised you about this position/industry?  

I think the thing that surprised me most was how challenging it was to find a job in the first place. I had expected that having a PhD in maths from KCL would mean I would find work immediately. However, the reality was that it took a lot of time, dedication and rejection.

The feeling of ‘switching off’ is also surprising. My PhD research never had an endpoint; there was always the next puzzle or the next little issue to solve. When working with a client, once the project has been delivered you can get a full and clean mental break from the work.


Do you have any tips for people interested in working in your area?

I would say read about data science techniques and in your spare time practice on real data. Be warned though: it’s one thing to read about and even implement a complex technique – it’s an entirely different challenge to clean data and be able to read code. I think people probably jump into trying to construct a neural network before they know what a class is or how to prepare the data.


Has your job/environment changed during Covid-19, and if so how?

We all basically work from home now. On the negative side, there are communication challenges – as a Data Scientist, I really miss having a whiteboard when trying to solve a problem as a team. I also miss the socialising with my colleagues. However, there are positives: I now save nearly two hours a day from not commuting, leaving me time to exercise and read more. I’ve also largely traded my jeans for pyjama bottoms, which has made work pretty comfortable.


Interested in transitioning from academia to data science? Read more about the Faculty Fellowship here.