Kailash Ballu

Director, Head of Data & Analytics, eCommerce, Financial Markets at Westpac Institutional Bank
Summary
Kailash Ballu is an accomplished data science and management professional with extensive experience in various industries. He has a proven track record of delivering successful projects that have increased revenue growth and reduced marketing costs through better understanding of customers. Kailash has a deep understanding of big data strategy, vision, and real-time dynamic pricing algorithms that have helped his clients to experience double-digit revenue growth. He has a unique perspective when it comes to data science, and his clients have benefitted from his expertise in web analytics, neural networks, machine learning, customer segmentation, and many more.
Kailash Ballu has both management and technical qualifications and has studied pre-doctoral studies, electrical & computer engineering, Masters in Electrical and Computer Engineering, MBA in finance, and marketing. He is an accomplished professional with a sound understanding of the financial sector. In his current role as Director, Head of Data & Analytics, eCommerce, and Financial Markets at Westpac Institutional Bank, he manages a successful team that delivers strategic insights to improve customer satisfaction and increase revenue. Before this, he worked as Head of Data Science - Global Centre of Excellence at SAI Global, Senior Data Scientist / Head of Analytics at RentalCover.com, and Consultant - Data Analytics Lead at Servian.
Kailash's unique background in data science and management has helped him to invent and file patents in the fields of big data and pattern recognition. He has also conducted due diligence to raise funds from venture capitalists and implemented pattern recognition engines for feature detection and identification. Kailash is an expert in dynamic real-time pricing, Monte Carlos simulations, Value at Risk (VaR) modeling, and market basket analysis. He is also skilled in propensity choice, linear and logistic regressions, Support Vector Machines (SVM), Decision Tree Classifiers, K-Means clustering, K-nearest neighbors, A/B Testing, customer lifetime value, dynamic pricing, and many more.