About

Rahul Kumar is a highly skilled and experienced Data Scientist with over 9.7 years of experience in the field. Currently working as a Lead Data Scientist at PayPal, Rahul is a proactive and diligent professional with a result-oriented approach. He is passionate about delivering the best results for his organization and personal growth in every dimension. Rahul's responsibilities at PayPal involve understanding the pain points of the business by structuring problem definitions with a hypothesis-driven approach, identifying key gaps and challenges, and implementing machine learning models with the MDLC framework as a solution to recommend data-driven actionable insights. He has worked on a wide range of problem areas, including Supervised Learning, Unsupervised Learning, Multitasking Learning, Semi-Supervised Learning, OpenSet Recognition, and Anomaly Detection. Rahul's interest areas and expertise include Customer Behavioral study and representation for generic downstream tasks, MultiModal Learning and its challenges, Predictive Uncertainty Estimating for bagging-based models and neural networks, Recommender Systems Design for retrieval and ranking-based systems, and Natural Language Understanding and Language Modelling. Rahul possesses a wide range of skills, including Machine Learning, Deep Learning, Natural Language Processing (NLP), Recommender Systems, Statistics, Data Analytics, Data Structures, System Design, and MLOps. He is proficient in Python, MSSQL, and R, and has experience working with tools and frameworks such as Tensorflow, Pytorch, and PowerBI. Rahul is also well-versed in cloud services such as Vertex AI and Microsoft Azure. Prior to joining PayPal, Rahul has worked with Ericsson as a Data Scientist 2 at GAIA Ericsson Research, where he developed machine learning as well as deep learning-based applications and frameworks based on business problems like Energy-based optimization of cellular network, predicting Orderfallout, and determining its root causes. He has also worked with Bosch as a Senior Engineer ML, where he created solutions to find anomalies of high-dimensional data using the technique of compression and expansion neural network along with Gaussian Mixture model called as Deep Auto Encoding Gaussian Mixture Model. Rahul has a Bachelor's degree in Electrical, Electronics, and Communications Engineering from SRM University.