Sophie works at MonoNovo Finance as a Modelling and Decision Science Manager. Here she shares her story on how she began her career in Data Science and how it has evolved over the years.
Sophie Pierson
I was always strong in maths at school but thought it would be too boring to study full time at University. I instead went to Bristol University to pursue my main love at the time, foreign languages. My thinking was that smart people still get jobs in finance and get trained on the job, but if I was multilingual I could travel the world with it too.
I found myself feeling jealous of those around me who were doing more scientific subjects and at the end of my first year realised I’d really made a mistake, and whilst I loved my current course I knew I could achieve a lot more if I switched.
That took me (through clearing) to study for a BSc Applied Statistics at Plymouth University in 2009. Pure maths was less appealing to me, so it was great to find somewhere offering courses weighted towards stats. This was more unusual at that time. Unsure of what to do after that, I decided to work for an MSc at Cardiff University in Applied Stats, Operational Research and Risk. Ironically, learning programming languages satisfied the same part of me which had driven me to study languages years ago, especially R.
The Credit Risk Scoring elements of the MSc also really appealed to me and I was lucky enough to land a job at Capital One in Nottingham as a Programmer, in the team which dealt with scorecard models. This role developed into Statistician, into Data Scientist and then to Senior Data Scientist. I had the opportunity to regularly travel to head office in Virginia (often via NYC or Washington DC, so I did get some travelling in there after all) and learnt a whole lot more about how a hugely successful data-driven company innovates, makes decisions and governs those decisions.
We had hackathons and meetups, built an R package which is now in CRAN, trained new analysts in regression and got to play with Hadoop (back when Hadoop was the ‘in’ thing!). I also got sent on an external ‘Data Science bootcamp’ which, if anything, showed me how much my degrees and experience had already taught me, despite considering myself a statistician first and foremost.
My next challenge was at a start-up bank in London as the sole Data Scientist. I was responsible for data mining customer’s screen-scraped financial data and designing the back-end app functionality to push valuable insights back to the customer. That was really fun and an interesting challenge which gave me a stronger appreciation for data architecture and engineering, as we had to keep redesigning the ways we stored data in order to compute it adequately in real time.
I then left London and the fast-pace of start-up life to move to MotoNovo in Cardiff in January 2018. I now manage a team of analysts who use SAS on a daily basis, though we are moving more and more to R as we push for more sophisticated visualisation and modelling techniques. We’ve created multiple production-ready decisions trees, scorecards, rulesets and statistical tests alongside Google Analytics, dashboards and customer profiling.
As this year we started taking on more project work and we have expanded a lot to meet the new demand. In the hiring process, we see applicants from a wide range of backgrounds and do not look for one ‘type’ of applicant; I feel it is important to hire for potential and passion where possible. I do not think this is always the case with data science, where the most important move can be getting your foot in the door. This is, in part, because of the fierce competition out there, despite the skills shortage!
If you’re looking to become a data scientist, you probably already know what your ‘specialism’ will be. It’s a bit like The Great British Bake-off; even the winner won’t be amazing in pastry week, bread week, cake week AND biscuit week.
Don’t be put off because your statistical knowledge has a soggy bottom, but your data engineering skills better be a show-stopper make up for it. It’s so important to keep on top of the trends, because it’s a fast-moving area a degree or training course can quickly become out of date, and honestly the very best data scientists eat, sleep and breathe what they do.
If you are passionate about having a truly fascinating and rewarding career in industries that can offer fantastic benefits, then it is worth the time commitment and I encourage you to follow your dreams it’s never too late.
If you want to find out more about the programme, or if you are a financial services businesses interested in joining the consortium, contact the Project Team at careers@cnectwales.uk