9 minutes

Dr Matt Crooks

Details of PhD

Mathematics, University of Manchester 2014.

Current position

Data Scientist, BBC.

Job highlight

Going on a placement to BBC’s Research and Development (R&D) department.

Case study conducted

May 2020.

What’s your background?

I spent 8 years studying at the University of Manchester, initially for a Master of Mathematics (MMath), before going onto an Applied Maths PhD. I went down the route of specialising in Applied Maths because I liked the fact that it related to real problems, but, despite my PhD project being about modelling earthquakes, it was still quite theoretical.

So, after my PhD, I left the Maths Department to do a 3-year postdoc in Atmospheric Science (also at the University of Manchester). Although I was doing mathematical modelling of Cloud Physics, the new department and the EU project I was part of were very multidisciplinary. This allowed me to work much more directly with real data as well as collaborate with the Met Office.

Why did you move beyond academia?

Like with a lot of people, it came down to a lack of opportunities in academia. I was settled in Manchester and didn’t want to move, but if I had been offered another postdoc at the University of Manchester I would probably have taken it. So, it wasn’t completely my choice to leave, but I valued stability perhaps more than wanting to be an academic.

Why did you choose the sector you’ve moved into?

Data Analyst was a job that I was aware of, but hadn’t really appealed to me. I’d applied for some roles after my PhD and had friends from undergrad working in these kinds of roles. Although I was keen for my work to have an impact, these jobs never seemed challenging enough and I was worried I might get bored.

By the end of my postdoc, Data Science roles were in vogue and promised much more challenging work and often asked for applicants with PhDs. They also require a high level of computer programming, which was a skill I spent a lot of time in academia learning and was keen to continue.

How did you get this job?

My fiancée works at the BBC and found out about the job. I also managed to get a phone call with the hiring manager through her boss to find out more about the role, the team, and some of the projects that they work on.

This really helped me to tailor my CV and my answers in the interview – it’s not just about having the skills, but also knowing how to apply them that wins extra marks in the interview. I’ve found most people are more than willing to help; reaching out to a connection on LinkedIn who works for the company to get a bit more information about a role can be really helpful.

I had to initially apply directly on the BBC’s careers website and then had to do a technical test for the interview. This involved being given some data and asked to present something back. Again, having a better idea of the types of problems that the team typically work on helped me to choose what analysis to do with the data and present at the interview.

Although the job descriptions for Data Scientist roles often say a PhD is preferable, it was clear from all the interviews I had that the interviewers had little experience with academics and most didn’t know what a postdoc is. So, it’s really important to translate and explicitly highlight your relevant skills, rather than just assuming that your postdoc position will be interpreted as you being a confident public speaker with a wealth of experience writing project briefs.

How did your postdoc prepare you for your current job?

I think being a Data Scientist is pretty similar to my postdoc. During my postdoc I was working with data, building mathematical models, and doing lots of programming, which is exactly what I do now.

During my postdoc I programmed in MATLAB and Fortran, while I now use Python. Although it wasn’t difficult for me to transition because the programming languages are all quite similar, it was challenging convincing hiring managers of this. I wish I had spent more time with Python sooner so that I was more confident in the early interviews.

Presenting work is also important in both a postdoc and in industry, although the audience is different. In academia, conferences are usually filled with experts from the same field. However, the best talks I ever went to were the ones that had clear visualisations and presented interesting results, rather than overly complex mathematical methods.

These are the skills needed when presenting in industry. You need to be able to find the interesting and relevant results so that stakeholders are engaged as well as build confidence that the methodology is robust without going into too much technical detail.

Data Science projects are typically 3-6 months long and the success is measured on being able to deliver the results. It’s important to agree with stakeholders what deliverables are achievable in the timeframe at the outset of the project.

When planning how you’re going to achieve the objectives, it’s also important that you have an idea of what work you’re going to perform as well as making backup plans in case those methods don’t work. This is very similar to writing a grant proposal. So, although your grant might not be funded, you’re still learning valuable transferable skills.

What were the first few weeks of being in your new role like?

I think when I started, I expected it to be more different from academia than it was and so lacked confidence to carry on doing things in the same way as during my postdoc. From a technical skills perspective, it was an easy and quick transition to make but I’ve definitely learned new and better ways of working that have made me more productive.

Can you describe a typical week in your job?

I have a few regular meetings scattered throughout the week. I have meetings with different groups of data scientists and analysts to discuss work on particular projects that we are collaborating on. I also go to a Machine Learning interest group and a Data Science reading group, where we discuss recent academic papers and new algorithms or methodologies.

I have meetings with my line manager once a week as well. The rest of the time I’m working independently doing Data Science. I feel it’s quite similar to what I did during my postdoc with a good balance of meetings and technical work.

Have you found the workplace culture to differ from that in academia?

The BBC is definitely a lot bigger than academia. I used to share an office with about 10 people and I’m now on an open plan floor with maybe 100. But we hot-desk and move around which I really like from a social point of view, although generally the different teams tend to sit on the same bank of desks every day.

Everyone at the BBC is really friendly and even the senior managers are approachable. There is a great work-life balance with flexible working hours and the option to work from home. There’s lots of internal training courses in both technical skills and soft skills, as well as plenty of resources and support for mental health and wellbeing.

Do former postdocs get hired in your company often?

Yes, quite a few. There are 4 people on my team with PhD or postdoc experience. It’s a valued experience in Data Science and I’ve definitely found my job easier because of the time I spent in academia.

Is there anything you miss about academia?

Working in academia felt like a lifestyle and part of my personality came from being an academic. Being a data scientist feels like it’s simply a job, which has the benefit that it is much easier to leave work behind at the end of the day.

Any advice to postdocs considering a career beyond academia?

Don’t be afraid of leaving academia; it’s not as different as you might think. I know a lot of people are worried that outside of academia you don’t have any independence with your work. However, managers like people who work independently because it makes their job easier.

I’ve also found that I’ve been given more responsibilities quite quickly because of my prior experience in academia. In many ways, not having to constantly apply for new grants and postdocs gives you time to just enjoy the work.