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Life at Featurespace:

Michael Varley, Data Scientist 

In his final year at University studying Physics, Michael became interested in machine learning and decided to pursue a Masters in AI with a focus on statistical and probabilistic machine learning. Coming out of his MA, Michael wanted a role where he could apply machine learning in an industry-based role and through his university careers opportunities platform he found Featurespace.

I wanted a role that applied machine learning in an industry where it's pertinent and applicable but also wanted to tackle interesting problems. Financial crime is a problem that has huge social ramifications and Featurespace works to solve this which is both interesting and a very legitimate application of machine learning, so it was a natural choice for me to apply for the role as Data Scientist. The role was exactly what I was looking for: it was focussed on machine learning and had the opportunity to develop my ­­­­programming skills all with the aim to solve a very important and challenging problem.

A day in the life of a Data Scientist at Featurespace

For a new graduate coming into the workplace and learn directly from industry professionals is invaluable, it's a great start to your career. There are many bright, enthusiastic and dedicated members of the team; it's easy to discuss difficult challenges with them and that's what makes this a great place to work. It's also a very trusting environment - you're given a lot of independence right from the start which quickly develops into responsibility at a pace that suits you. Looking through the data sets and combing through the detail to find out what will help us to block fraud is often challenging. But having the opportunity to learn new techniques and fields that are applicable to what we're doing is really great. We're constantly adding to our knowledge.

In addition to training models that catch a lot of fraud, one of the things I've really enjoyed learning about is additional problems that are important in a commercial environment. For example, the interpretability of models and understanding why those models make those decisions and how it relates to the customer.

For any graduates planning a career in Data Science

Take the time to explore areas of machine learning in the field you are interested in! Taking part in competitions and projects is also really important. There are some great resources and competitions out there, such as Kaggle. At an interview, people are always keen to hear about what you've done independently to further your knowledge and the way in which you applied your learning to these projects.

 

Interested?

If you are a bright individual who is passionate about innovative technology used across the world to fight fraud, we'd love to hear from you!

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