.About Nubank Nubank was founded in 2013 to free people from a bureaucratic, slow and inefficient financial system. Since then, through innovative technology and outstanding customer service, the company has been redefining people's relationships with money across Latin America. With operations in Brazil, Mexico, and Colombia, Nubank is today one of the largest digital banking platforms and technology-leading companies in the world. Today, Nubank is a global company, with offices in São Paulo (Brazil), Mexico City (Mexico), Buenos Aires (Argentina), Bogotá (Colombia), Durham (United States), and Berlin (Germany). It was founded in 2013 in Sao Paulo, by Colombian David Vélez, and cofounded by Brazilian Cristina Junqueira and American Edward Wible. For more information, visit www.Nu.Com.Mx. Data Science at Nubank At Nubank, we aim to empower our customers to take control of their financial lives. The Data Science team develops models and leverages its expertise to provide the best experience and products, using statistics, Artificial Intelligence, and lots of creativity to predict our customers' behaviors. Our team strives for cutting edge model development techniques, from Machine Learning to Reinforcement Learning and beyond. We're partnering with business and technology to make the speed of thought decisions possible. As a Data Science Manager, you're expected to: Develop and grow high-performing teams of data scientists and ML engineers. Define the vision of the team and help the team deliver on the vision by setting clear goals and objectives, providing information and context, clearing obstacles, brokering consensus, and working quickly to close gaps in key resources and skills. Attend to the team and individual health and performance by safeguarding the team's psychological safety, providing clear, specific, timely feedback, taking decisive actions to manage performance, advocating for recognition of the contributions of the team, and protecting the team from unproductive pursuits. Conduct performance reviews, participate in calibration, solicit feedback, and generally perform the administrative functions of people management. Translate the business needs, define roadmaps, and lead projects composed of cross-functional teams to deliver results. What are we looking for? Strong technical backgrounds in disciplines related to data science. Good understanding of the software development lifecycle and popular tools used in modern systems. Understanding of the data science modeling life cycle including data preparation, model training, model management, logging, and monitoring of models in production. Excellent problem solvers, adept at working across teams of engineers, analysts, product managers, and business leaders. Ability to help teams prioritize work, make smart, timely decisions, and execute at a high level of professional skill, quality, and speed