Informed.IQ
Nikhil Kumar has 3 years of work experience in the field of Machine Learning and Data Science. In 2021, they began working as an Applied ML Engineer at Informed.IQ, where they work on solving problems such as document identification, signature detection, and income prediction using cutting-edge AI technology and frameworks such as Pytorch, Detectron2, Yolov5, and Huggingface. In 2020, they worked as a Machine Learning Engineer at W.R. Berkley Corporation, where they built a small claims ensemble ML model on unstructured FNOL data using CatBoost model, XGBoost, Python, SQL, and Seaborn Plotly. This model was deployed on AWS. In 2018, Nikhil worked at Automation Anywhere, where they worked on OCR Evaluation using OpenCV, Pandas, and Python, as well as Document Extraction using Object Detection, OCR, and NLP. Nikhil also worked on the MERN stack and gained hands-on experience with Robotic Process Automation.
Nikhil Kumar attended George Mason University from 2017 to 2019. In 2017-2018, they completed a Master's degree in Mathematics and Statistics.
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Informed.IQ
Informed helps banks and government accelerate their digital transformation with turn-key robotic process automation capable of performing real-time income, asset, identity, residence, and insurance verification tasks. Banks and government use Informed to reduce their manual costs associated with reviewing documents, catch fraud, and cut down on errors when originating loans, opening accounts, and administering benefit programs.