Created roadmaps and managed analytical projects across multiple stakeholders by balancing current priorities and future needsLed the transformation of data science technologyMentored and developed a data scientists teams

Trained the analytical community and data scientists in R, Python, Spark, and Machine Learning.Motivated technical sharing sessions using a do-and-learn philosophy.Enabled migration of SAS based legacy codes to Python/ R and Spark.

Designed several interactive reports to identify key drivers of KPI metrics that track customer engagement over time enabling the business to stay on top of the trend and check on the health of customer engagement.Built algorithms using a cohort of customers to chart the different journeys and predict the path of new customers in the early phase of their journey.Enhanced the well-known Traveling Salesman Problem solution to improve flow adjacency of categories in a store, and improved the shopability score of customers by 5%.Developed availability solutions that identified out of stocks of products on shelf and quantified lost sales opportunity.Identified growth of 3% by developing space elasticity models and simulated annealing algorithms for optimization of shelf space

Enhanced rules-based pricing systems to include optimization driven pricing strategy and communicated the value of optimization by A/B testing.Built new solutions leveraging the model parameters to answer key business questions and generated 6% incremental uplift in sales.Segmented customers from transactional data using multivariate analysis techniques to design various experiments and test different features.Developed feature extraction and feature selection utilities for data from various sources such as for planograms, transactions, promotional data feeds, products feeds, and third-party data.