6 questions for Artem Kozhevnikov, Lead Data Scientist at Tinyclues

Data Scientist
Caroline Tailleferd

VP Marketing

10 January 2017

After studying mathematics at the State University of Novossibirsk in Siberia and then at École Polytechnique in France, Artem Kozhevnikov joined Tinyclues in 2010 as a Data Scientist intern. He completed his thesis in mathematics in 2015 and is now Tinyclues’ Lead Data Scientist. In this interview Artem describes his job and main responsibilities at Tinyclues.

When did you join Tinyclues?

I joined Tinyclues in 2010 as an intern. At that time I worked very closely with David Bessis, Tinyclues’ CEO and Founder. My job is to build the machine learning algorithms and the artificial intelligence (AI) technology which is at the very heart of our solution.

What are your areas of responsibility as a Lead Data Scientist?

I manage all projects related to Data Science, and there are quite a few! We are constantly working to improve our solution and provide our clients with new features. These new features are built from suggestions from our internal Product team and are always based around advancing in the technology at Tinyclues’ core: its algorithms. I oversee all Data Science subjects which enable these new features to be created.

Ultimately, I have a very varied role. My team works on many types of projects, from evaluating the performance of a calculation method, to assessing A/B Testing protocols for a client. All of this is in addition  to  our core business: the development of predictive models.

What is a typical day like as a Lead Data Scientist at Tinyclues?

Hal Varian, Google’s Chief Economist, recently said: “The sexiest job in the next 10 years will be a statistician. And I’m not kidding.” I fully agree with him: my job is fascinating! A huge amount of my time goes into managing Tinyclues’ other Data Scientists. I also work with most of the company’s other teams on a daily basis, including the Product, Engineering and Operations teams.

In addition to managing technical projects, I monitor new and emerging methods, the latest algorithms and predictive models. Identifying how to add these to our solution is a critical part of my job and how we ensure Tinyclues is always at the cutting edge of technology! I also love coding but I have less and less time to do so…

What are the skills and qualities required to do your job?

A very good level of mathematics is essential to understand and work on our predictive models. You also need to be able to program and read many different coding languages. Additionally, having an advanced knowledge in computer science and machine learning can not be dismissed. Last but not least, being a Data Scientist requires a real ability to teach in order to translate mathematical concepts to more or less expert stakeholders.

Our clients’ success is our priority and we must always keep in mind that we are developing solutions for them. This means understanding their needs, their backgrounds, their resources and their goals. Seeing our technology have a  positive impact on our clients’ businesses is our ultimate goal.

What do you particularly like about Tinyclues?

Tinyclues masters a very high level of scientific culture. Moreover, our corporate culture promotes curiosity and asks us to “think outside the box”. I am lucky enough to interact with people from all parts of our company and it is very enriching. I don’t think all Data Scientists have that opportunity. For instance, each week we arrange a meeting about Data Science, open to all coworkers, no matter their jobs.

What changes could be expected in your field?

The current market has an ever increasing demand of workers with expertise in Data Science, and it’s just the beginning! Within 10 to 15 years much more accurate and specialized solutions based on AI will be developed. Currently, the term ‘Data Scientist’ is an umbrella term for many different jobs. However the future will see more specific missions emerging, focused either on the ‘product’ side or on the ‘technical’ side. This specialization will go hand-in-hand with the emergence of other application fields for data, still unknown as of today.

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