The challenge with automating healthcare technology is that the stakes are very high, since lives are on the line..— Sujit Pal, Technology Research Director
I use different Machine Learning and NLP techniques to help make our products more valuable to the people who depend on them.
When I was growing up in India, I was always interested in mathematics as a subject and a keen interest in numbers – that was what led me to pursue a career in analytics.
In my role as an NLP Specialist at Elsevier, I use different Machine Learning and NLP techniques to help make our products more valuable to the people who depend on them. For example, Elsevier collects lot of feedback from authors, publishers, and editor. That information can be used in various ways, but we need to process it carefully to gain insights, so one of my roles is to use Machine Learning and Natural Language Processing to help us identify what that feedback is really telling us.
Before joining Elsevier, I worked at start ups – but Elsevier has given me a new understanding of how things scale in a global organization. One of the key things that drew me to the company was the chance to work on challenges that would have a direct impact on the products people are using.
That’s actually one of the best parts of working at Elsevier. Teams are formed such a way that you have enough space to be creative and make a difference, and at the same time enough guidance to learn new things. Ideas are openly received and new initiatives are encouraged, and there’s an open culture to discuss, debate and arrive at the best possible solution
Meet others in the team
What I like about working on machine learning here at Elsevier is that we can combine theory with practice..— Georgios Tsatsaronis, Principal NLP Specialist
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