commercetools does not only build a smart eCommerce platform but also is a group of visionary minds who enable brands and retailers to create inspiring shopping experiences every single day. We are a global company with more than 150 eCommerce professionals coming from 35 countries worldwide and work for our offices across the globe. As you can imagine, there are a lot of different roles and responsibilities in our teams. Not every job title is self-explanatory – so let’s take a closer look!

Amadeus Magrabi explains what a Data Scientist does at commercetools.

Data is often compared to a gold mine: There is a lot of value that can be gained from it, but it is hard to find. Simplifying somewhat, data scientists dig for gold in the data of a company. In particular, this means we use methods from the field of machine learning to detect hidden patterns in data, which can then be used to make valuable predictions.

This approach has been successful in many different areas and is transforming virtually all industries. It can be used to predict which websites people want to see, automatically drive a car, recommend what songs users will like, detect cancer in CT images, predict on which ads someone will click, or send warnings for upcoming natural disasters. In general, whenever you can collect data on a phenomenon, you can make predictions about it.

At commercetools, we don’t use machine learning to predict natural disasters, but to avoid technological disasters. Our overall goal is to make managing a commerce platform easier, more efficient and reduce the amount of annoying manual work that e-commerce managers frequently face. For example, we develop features to automatically sort products into categories, identify objects in images, increase data quality, detect cases of fraud or predict future demand.

What I like most about my job is the combination of experimental research and software engineering and the fast pace of the field. Machine learning researchers constantly come up with new ideas that challenge the current status quo and open the door for exciting new applications. This is why an essential part of the job is to discuss different approaches and share experiences with colleagues and data scientists from all over the world (at conferences, via blog posts or of course by yelling at each other on twitter).

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