How did you change careers into data science?

  • 13 April 2022
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Working at Peak, we have such a diverse team of data scientists, and I’m always interested in hearing about people’s routes into data science, particularly the less conventional routes! 

I worked in various roles in business intelligence and data engineering before deciding to move into data science, and I did a two year part-time MSc in data science to get me into my new career. This got me up-to speed with the maths and statistics knowledge that I lacked from my non-technical undergraduate degree.

What career(s) did you have in a previous life? I’d love to hear about how you made the transition into data science and what skills from your previous jobs were transferable?


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Before joining Peak I was all ready for a lifelong career in academia. Half-way through my PhD in Epidemiology I realised that academic life is not for me. I wanted to work in a fast-moving and exciting industry but still be able to use my research, problem solving and coding skills. I think that my biggest issue in changing careers wasn’t proving that I can be a data scientist but realising that I actually do have the necessary skills myself. It can be so difficult if you don’t have a traditional STEM background to realise that there are many many paths to working in tech. Something that helped me was listing every single project I ever worked on (including in my spare time and during my social science studies) and every single skills (no matter how small) I had to use/develop to complete it. It was an eye-opening experience! 

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Prior to studying my Master’s in Data Science, I was working in quite a different career. My Business Continuity role consisted of advising and enabling many different business units plan for major disruptions including Fires, Pandemics, Cyber & Terrorist attacks.. who would know that shortly after leaving that role that Covid-19 would hit! In my Business Continuity role, I had to understand all the major processes in the different teams so I got to speak to people in many different parts of the business. 

 

Since primary school, I’ve always enjoyed Maths and working with Data so I started talking to colleagues in data roles to understand their jobs. A few colleagues chatted to me about being a Data Scientist and the skills required to get into the role. At first it seemed that there were huge barriers - learning to programme, building up my statistics knowledge and learning about data science algorithms. I realised that I wouldn’t be able to learn all these topics in my current role or my spare time… So I started looking at different Universities to study Master’s in Data Science. Since then I haven’t looked back, I’ve loved every minute of this learning journey and hope it continues!

 

My advice would be to try to chat to many different people in Data Science roles to really understand if it’s the change in career that you’d like to take.

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@EmmaBellamy what a shift!! Have there been any parallels between the two careers that have surprised you? 

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My pathway to Data Science is one driven by a love of applied mathematics, and later on, a passion for programming. It looks something like this, Physics Bachelors > Software Engineer > Spacecraft Engineer > Data Science Masters > Data Scientist.

Throughout this journey I’ve always enjoyed solving complex problems and communicating the results, skills which align nicely with a Data Scientist role at Peak. Likewise, having discovered programming fairly late on in my Bachelors degree, my enjoyment of using, which began with automating and streamlining process, now aligns excellently with the end-to-end pipeline of building interesting solutions as a Data Scientist.

The hardest transition for me has been the move from slow (and archaic) processes and projects in established fields such as the Space sector, to the dynamic and fast-paced field of commercial Data Science. Previously I’ve worked on projects with the timeline of years, now I work on projects that have the timeline of weeks, if not days!! One approach that helped me here, is the use of task management tools and enforcing good working practices, such as defining a to-do for the day and using colour coding for prioritisation!

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