Speaker "Anando Sen" Details Back



Title: Improve customer experience through Multi-arm Bandit Subtitle: A Reinforcement Learning-based optimization


In order to accelerate innovation and learning, the data science team at uber is looking to optimize Driver, Rider, Eater, Restaurant and Courier experience through reinforcement learning methods. The team has implemented bandits methods of optimization which learn iteratively and rapidly from continuous evaluation of related metric performance. Recently, we completed an AI-powered experiment using bandits techniques for content optimization to improve the customer engagement. The technique helped improve customer experience compared to any classic hypothesis testing methods. In this session we will explain various use cases at Uber that this technique proven its value and how bandits have helped optimize and improve customer experience and engagement at Uber.


Anando Sen is the Senior Product Manager at Uber. He builds tools and processes to help Uber convert business goals to product experiments. Optimal decision making is dependent upon fine balance between exploration and exploitation of solutions. He owns data platforms that help with that balance and enables verifiably better decisions by humans and machines. His customers are teams in engineering, data science, design, marketing, business development and operations. The work he does affects everyday business at Uber and that keeps him motivated.