Data Scientist [All Levels]

Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia. For more information, visit our institutional page https://international.nubank.com.br/careers/

Data Science is essential to every part of the business at Nubank, since Day 1. Most areas of the company use data science to some extent. Data Scientists are responsible for translating business needs into models and other types of data-driven solutions.

• Interact with other team members to understand how to best apply data science and artificial intelligence to Nubank’s problems;
• Investigate problems and discover insights through exploratory data analysis and other quantitative methods;
• Have great numerical and analytical literacy, including knowledge of statistics and linear algebra as needed;
• Work on multiple stages of the ML model lifecycle–from ideation to training and steady-state operation;
• Work across multiple types of models, algorithms and learning paradigms;
• Communicate technical points, decisions and tradeoffs to peers and stakeholders;
  
  

• Has experience working with ML models in a production environment;
• Understands possible pitfalls and tradeoffs when applying ML to real-word, sometimes messy problems;
• Is familiar with the technical aspects of learning theory and algorithms and is able to discuss details if needed;
• Has experience working with the Python scientific stack (Numpy, Pandas, Scipy, Sklearn, Boosted Tree Libraries, etc);
• Has experience working with Tensor/Deep Learning libraries such as Tensorflow and Pytorch;
• Is able to connect analytical outcomes to business priorities and advise stakeholders on the best course of action;
• Has good communication skills and is able to convey technical topics and decision points to diverse audiences;
• Has experience writing general-purpose code using modern tools;
• Understands the implications of causality when applying ML to problems;
• Is up-to-date with recent developments in the area as a whole and is able to identify opportunities for improvement and innovation;
  
  

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/