About

Mayur Nawal is a highly skilled data scientist with a focus on machine learning, AI/ML, and signal processing. He has a strong background in Electrical Engineering, with a specialization in Communication and Signal Processing from the prestigious Indian Institute of Technology, Bombay. With a relevant experience of 6.36 years, Mayur has worked on various projects that involve creating end-to-end ML pipelines, data mining, cleaning, pre-processing, feature engineering, model selection/training, and deriving insights from the results. Currently working as a Data Scientist at Liquidity Group, Mayur is responsible for creating machine learning-driven products that help companies get instant, large credit facilities. He has also worked as an ML Research Fellow at Indian Institute of Technology, Bombay, where he successfully published two research papers in Journal of Petroleum Science and Engineering (J.P.S.E, Elsevier) and International Meeting for Applied Geoscience & Energy (IMAGE-2022). In his ongoing projects, he is leveraging deep learning approaches like 1D CNN for time-series prediction, auto sklearn, and scikit-learn for grain size prediction. Mayur has a strong technical skill set that includes Python, Numpy, Pandas, PostgreSQL, Sklearn, Keras, fastai, Seaborn, Matlab, and Tableau. He is the author of LithoBot, an AutoML approach to identify lithofacies from well logs. His expertise lies in Deep Learning, Machine Learning, Python, Pandas, Scikit-learn, Keras, fastai, Matlab, auto-sklearn, classification, regression, NLP, and time-series. Prior to his current role, Mayur worked as a Wireline Field Engineer at Schlumberger, where he managed land wire-line logging operations for private and state oil companies in Barmer, Rajasthan. His responsibilities included lead-engineer, tools preparation, logistics, base management, supporting field requirements, and occasional client relations. Mayur is a result-focused, disciplined, positive, and quick learner. He is an excellent team player and has a proven track record of delivering high-quality work within tight deadlines.