Keywords: AI, machine learning, enterprise teams, data science
Teams in industry are constantly dying because of bad structure, poor communication, and a lack of clarity. This is, in part, because we’re married to a homogenous structure. Data scientists sit with, work with, and answer to data scientists. Business people sit with, work with, and answer to business people. But what if these walls were torn down? When tasked to form the first a AI team T-Mobile, we opted to rewrite the playbook by sitting data scientists, software developers, machine learning engineers, and business people all at the same table. Our work and the products we build are deeply entangled so our daily work life should reflect that. Through this heterogenous structure, we’re able to communicate more quickly, design and develop high-fidelity, novel products, avoid miscommunication, and attack business problems with a diverse set of tools and ideas. In this presentation, I will work through how this team was formed, the struggles we’ve faced because of our diversity, and how we’ve overcame them, and how we’ve harnessed our differences to become a team so performant and successful that everyone keeps asking us “How do I join ?”