About

Divyanshu Sharma is a self-driven and business-oriented analytics professional with almost two years of experience in the field. He is currently working as an Associate Manager at PepsiCo, where he focuses on media analytics. Divyanshu has a demonstrated history of working with global CPG and Retail data for Fortune 100 clients, which makes him an expert in his field. Divyanshu has a strong background in data wrangling in Excel and SQL, with more than two years of experience. He also has four years of experience in R, where he performs statistical analysis, visualization, and predictive modeling. His specialties include econometric modeling, data mining, machine learning, and novel research methods. Divyanshu is proficient in programming languages such as Python, R, SQL, Excel, VBA, AWS, Tableau, and PySpark. He is also knowledgeable in algorithms such as Generalized Linear Models, Bayesian Model & Probabilistic Programming, Time-Series Models, Supervised Learning (Logistic Regression, KNN, Decision Trees, Ensemble Modeling), and Unsupervised Learning (K-means clustering, Principal Component Analysis, Factor Analysis, Density Estimation). Before joining PepsiCo, Divyanshu worked as an Analytics Specialist at Uber, where he was responsible for full-stack business analytics for South Asia's rider operations. He also worked as a Senior Data Analyst at The Smart Cube, where he solved problems with advanced statistics, machine learning, and deep learning by leveraging untapped data. Divyanshu holds a Bachelor of Engineering degree in Instrumentation and Control Engineering from Netaji Subhas Institute of Technology (NSIT), where he graduated in 2019. He also completed his high school education in Engineering from Manav Sthali School in India in 2013. Divyanshu's tech stack includes SQL, AWS, Supervised Learning, ML, Data Scientist, Python, Research Scientist, and Deep Learning. With 3.54 years of relevant experience, Divyanshu is a highly skilled and knowledgeable analytics professional with a passion for exploring, predicting, and contextualizing data.