What I like about working on machine learning here at Elsevier is that we can combine theory with practice..— Georgios Tsatsaronis, Principal NLP Specialist
Machine Learning Specialist
Elsevier’s content is like a huge playground for a Data Scientist to train their algorithms on.
Being a data scientist is like being an explorer in the 21st century – you’re often confronted by unexpected insights. That’s a standard part of the job – after all you call a Data Scientist when you don’t want to be guided only by heuristics and assumptions.
In fact, the data is a big part of what drew me to Elsevier. The company’s long history in the publication sector means it has data that goes back to several centuries - that’s like a huge playground for data scientists to train their algorithms on. Added to that, considering Elsevier’s transformation towards an information analytics company it is a very interesting time to be here.
My role is Machine Learning Specialist in the Content and Innovation Department based in Amsterdam. The team is mostly engaged in building data driven solutions that help to maintain or improve the quality of products within Elsevier. We call these solutions ‘POC’, which stands for proof of concepts. A typical POC would be the automated extraction of topics from Elsevier articles. Before that, I worked in several companies, ranging from tech-startups to large-scale companies, in which I mostly worked in data and analytic departments, building on my background in Economics and Data Science.
Possibly a good thing about working at Elsevier is the flexibility. There’s the fancy idea of a digital nomad, which are essentially analysts and programmers that can work from wherever it suits them. You can see them sometimes with their laptops in cafes. From a company’s perspective that is not so straight forward to established referring to all the technical and managerial burdens. However, Elsevier is really trying to make remote working possible.
Meet others in the team
The data that we have at Elsevier is a goldmine of information for a lot of interesting and intelligent things..— Deep Kayal, Machine Learning Engineer
If machine learning could improve the quality of science, this would have huge implications on the price and speed of drug manufacturing..— Helena Deus, Senior Technology Researcher