Amit is based in India, working at Insife since 2021 as a data scientist focused on ML/AI and as such he has played a key role in implementing SPARK ML/AI into the newest version of HALOPV delivering NLP.
Amit Arora is a data scientist, specialised in ML, AI, and NLP. When he decided to join Insife it was a strategic choice, based on his expert insights and rather controversial acknowledgement.
Amit has a Master's degree in data science and analytics from Royal Holloway, University of London, following a Bachelor's degree of Technology, Electronics and Communications Engineering from Guru Gobind Singh Indraprastha University in India, where his was born. His professional career has been focused on the development of analytical and predictive models – work which has included using ML (Machine Learning) and NLP (Natural Language Processing) – for industries such as aviation, insurance, and pharmacovigilance.
Today he is based in India, working at Insife since 2021 as a data scientist focused on ML/AI and as such he has played a key role in implementing SPARK ML/AI into the newest version of HALOPV delivering NLP.
Realising the true nature of IT
Asked to explain why machine learning and artificial intelligence became his area of expertise, Amit points to a fact which is a bit surprising coming from a data scientist:
“What we need to understand is that computers are essentially stupid, and to worsen the case they only understand numbers. Originally computers were developed using numbers – the numerical system. Logic, structured and governed by rules of consistency. And they have served us exceptionally well crunching the numbers. But now we are asking them to process something completely different. We are asking them to help us process the most confusing, incoherent, multi-faceted and diverse kind of data. We are asking them to process words and the languages we construct using them.”
According to Amit this is a problem, which especially can be seen across the bio and life science industry and the health care sector as a whole:
“We are dealing with people, diseases, symptoms, adverse effects, personal experiences and ways of communicating them. Handling these lays in the cross-field between natural sciences such as mathematics and biology and “softer” sciences such as human relations, language, and interaction. Sciences which are almost impossible to handle with digital algorithms in the traditional sense, and up until recently we had to do this manually”.
Why Drug & Patient Safety?
And this is especially acute when it comes to PV – Drug safety:
“To handle the growing amount of information and design solutions that enables us to process and translate it into data we can use to deliver patient safety we need to go beyond traditional computing. We need to use artificial intelligence and continuously educate and improve our solutions using machine learning.”
According to Amit this will bring a major relief:
“Using ML/AI will deliver such a massive acceleration of timesaving and reduction of our need for manually handling. This will – of course – be very beneficiary to the industry in terms of minimizing the operational footprint but with regards to ensuring and improving patient safety this can be a crucial turning point”.
Which brings us back to why Amit Arora chose Insife:
“I realised that if I wanted to play a part in what I consider to be one of the most interesting and progressive areas of IT I had to do two things. First: Choose the industry where ML/AI is most relevant – needed – and will probably be almost mandatory and second: Choose a company proving to be in the field of front runners: Using it and investing heavily in it to future-prove their market offering. Having realised this, choosing Insife was the obvious way to go”.
Before Insife:
WorkPersistent Systems / Shree Partners
Junior Data Scientist
Real Time Data Service
Voip Engineer
Education
Royal Holloway, University of London
Master's degree, data science and analytics
2018 - 2019
Grade: 2:1
Guru Gobind Singh Indraprastha University
Bachelor's degree of Technology, Electronics and Communications Engineering
2013 - 2017
Grade: 7.77/10