Meet Kévin, Data Scientist at Sublime

Hi, can you say a few words about you?

Hello, I am Kévin. I come from the South West of France: Pays-Basque (Basque Country). I am 30 and I love music (I play drums and guitar) and the sci-fi universe (books and series). I am a “bon vivant” and enjoy the company of my colleagues and friends over drinks and card games.

What makes you get up in the morning?

Above all… coffees(sss)!!! And I know that I will see my colleagues, exchange with them, share good moments (whether in professional or personal terms), learn things. The day usually goes by very quickly!

Can you tell us more about your position at Sublime?

I am a Data Scientist at Sublime; I joined the company 5 years ago. My job consists in creating and adapting algorithms and models to make predictions and propose some solutions to address business issues. I collect data from multiple sources ranging from statistical analysis to machine/deep learning, so it is a mix between math and IT.

Can you tell us more about your background and how you decided to work in Data science?

After a classic baccalaureate, I did a bachelor’s degree in mathematics at the university of Pau, followed by a master’s degree in mathematical modeling in Bordeaux. I acquired skills in modeling, high performance computing, image processing, data science, reliability and statistics, operations research, and decision support. With the rise of computers and storage performances, the job of data scientist is booming and allows us to work in multiple areas.

Why is Data Science so important for companies and organisations?

First, without data in our ecosystem, we can’t do much. Furthermore, data science is used to anticipate potential fluctuations of metrics in time. For example, in our case, we created statistical and big data methodologies for predictive fraud propensity models and use them to create alerts that help ensure timely responses when unusual data is recognized.

And finally, what do you like best about your job?

With this job, we must adapt our skills to solve business issues. I must manipulate, clean, modify data to use the right prediction model, so each day is different and it’s always a challenge. Moreover, data science is in constant improvement, so we must keep abreast of the latest news for performance.