About

Nirbhai Singh is a skilled Machine Learning Practitioner with over 6 years of experience in the field. He has a strong background in developing algorithms for natural language processing, predictive models, semantic analysis, anomaly detection, and machine translation, using both supervised and unsupervised Machine Learning techniques. Currently working as a Data Scientist III at Honeywell, Nirbhai is responsible for conceptualizing and architecting a framework to reduce the development time of prediction use cases for data scientists across the company. He has also built a random decision forest model to detect anomalies in sales transactions, which has helped save over 1 million USD by improving master tables for pricing and reducing cases of price overrides. Additionally, Nirbhai has improved the search feature of a customer-facing dashboard using lda2vec for topic modeling and BERT sentence embeddings, leading to an increase in self-servicing and an 18% decrease in the number of service tickets created. In his previous role as a Data Scientist II at Honeywell, Nirbhai developed a prediction algorithm to create a regression test suite based on historical time series testing data. He also architected and implemented a time series model using Markov chains to predict labor and non-labor cost for Honeywell projects. Furthermore, he led a team of four data scientists working on NLP aided requirement authoring, where they parsed system requirements, converted them into UNL and SRS, and then built formal models using PVS to find incompleteness and disjointness in requirements. Nirbhai holds a Master of Technology (M.Tech.) in Computer Science from the Indian Institute of Technology, Bombay, and a Bachelor of Engineering (B.E.) in Computer Software Engineering from Thapar University. He is well-versed in various technologies, including Research Scientist, Data Scientist, ML, NLP, BERT, language models, test, and Search. Overall, Nirbhai has 6.45 years of relevant experience in the field of Machine Learning and has proven to be a valuable asset to his current and past organizations.