About

Badhri is a highly skilled Data Scientist with almost nine years of relevant experience and expertise in using Bayesian techniques and Probabilistic Graphical models to model inference problems. He has a Ph.D. in Electrical, Electronics and Communications Engineering from the Indian Institute of Science (IISC) and an Engineer's degree from RV College of Engineering (RVCE). Badhri has worked on various aspects of demand-supply fulfillment chains in the Rivigo marketplace, where he was a Data Scientist. He was part of the team that developed a user profile store to support various use cases, such as lane recommendation, CLV scoring. He also developed and deployed a pricing model based on Cascaded Logistic Regression to predict the first trip price of non-liquid lanes. Additionally, he was part of a team of two that developed a Model Pipeline library to streamline the model features access, model train and scoring processing. Badhri also has experience working with Near.co as a Data Scientist. He worked on developing scalable algorithms related to Audience Segmentation and Segment Composition Estimation using various machine learning techniques. He was part of a two-member team that designed and developed a highly-scalable Data Science Spark pipeline, capable of handling billions of user ping data spread across 20+ countries, to prepare the features for modelling and to compute the model outputs, which power the analytics in the product. He also developed an algorithm to infer the behavioural profile of the user based on the POIs visited, age and gender of the user. Currently, Badhri is a Senior Manager - Data Science at Swiggy, where he applies various models and techniques from Bayesian inference, Stochastic Processes, and Statistical Learning Theory, such as Hidden Markov Models, Bayesian NMF, Reinforcement Learning techniques to come up with appropriate mathematical models to represent location data and answer various questions related to location intelligence for providing consumer insights, audience targeting, location-based audience profiling, and estimation of audience composition. He has hands-on experience in Data Extraction, Data Visualization, Data Analysis, and building models, in a fast-paced start-up, working on big-data systems. Badhri's tech stack includes Data Scientist, Research Scientist, Search, Reinforcement Learning, UI, NLP, ML, Python, Spark, iOS, and Web. He has a passion for statistical theory and good designs, which has enabled him to model the inference problems, come up with the algorithms and build 'scalable' and 'reusable' systems that