Member since 1 month
I've been working as a data scientist for about two years now with RedHat Inc., I was previously a full stack developer working for the Infoedge group in their 99acres.com division. In my two years at RedHat I've worked extensively on building and maintaining production grade recommendation systems. We follow the "devops" development strategy at RedHat and as a data scientist for all my projects I am responsible for the entire pipeline, all the way from data collection to deploying my model using microservices and maintaining these production service running in containers on Openshift(Kubernetes) which has enabled me to gain a lot of practical industry experience and expertise around the same. I also have to self manage my AWS and am quite skilled with the AWS suite of services (well, a lot of them).
In terms of my machine learning projects, as mentioned previously I have worked extensively on applying recommendation techniques to rather un-orthodox use case of dependency analysis. I am also one half of a patent around using deep learning based technique for smarter dependency recommendations. In my undergrad work I was very interested in computer vision and NLP, and worked on multi-document summarization and image captioning(this was in 2015 when these areas didn't have the same mainstream appeal they do now, neither was Tensorflow a thing). I have used various NLP techniques such as LDA to solve problems at RedHat and am currently working on a problem around smarter facilitation of over the air upgrades for RedHat product(s).