Retail Credit Risk Specialist

Join to apply for the Retail Credit Risk Specialist role at Nubank . Get AI-powered advice on this job and more exclusive features. About Us 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 About The Team The Credit Risk Squad is part of the 2nd line of Defense, responsible for managing and overseeing the credit risk function of our organization by providing independent oversight and challenge to the first line of defense. As a second line Credit Risk team, we are responsible for creating Expected Credit Losses (ECL) models that are used to calculate loss allowances, ensuring the company’s credit risk is managed correctly and efficiently, participating end-to-end in the model development process, including implementation, monitoring, ensuring operational quality of the data and supporting Finance accounting teams. About The Role As a Credit Risk Specialist within our Credit Risk Squad (part of the 2nd Line of Defense), you will play a crucial role in supporting Nubank’s world class risk management framework. Your primary focus will be on expected credit loss models development and maintenance, particularly within the retail market portfolios, including credit cards and personal loans. Your Key Responsibilities Will Include Developing, implementing, analysing and monitoring risk parameter models (PD, LGD, EAD) relevant to our retail portfolios, ensuring they align with regulations like IFRS9 and 4966. You will be instrumental in making sure we have state of the art models also through activities such as backtesting, data analysis, documentation and operational monitoring. Maintaining a strong understanding of relevant local and international regulations concerning provisions and credit risk management, including a surface‑level understanding of standards like Bacen 4966 and IFRS9. Performing in‑depth analysis of credit risk exposure and its implications for expected credit losses (ECL) and loss allowances. This will involve understanding how changes in the credit portfolio impact our provisions. Participating in monthly accounting closing activities, contributing to the creation of reports on loss allowance movements. Actively contributing to the enhancement of our controls environment within credit risk management. This is crucial for ensuring compliance with regulations such as SOX. Liaising with Internal and External Audit teams during model validation processes, ensuring adherence to regulatory requirements and SOX compliance in our operational processes. Leveraging your analytical and problem‑solving skills to manipulate and learn from data related to the credit portfolio. This role requires a strong analytical background, a keen understanding of credit risk principles, and the ability to contribute to a robust and compliant credit risk management framework. Your work will directly impact the accuracy of our financial reporting and the overall stability of our credit operations. Basic Qualifications Bachelor’s degree in Engineering, Economics, Math, Statistics, Physics or related fields. Analytical, problem‑solving and passionate to manipulate data in order to learn from it. Great coding skills (SQL, Python, Scala or other similar programming languages). Experience in developing or analysing outputs of Expected Credit Loss models/PD/EAD/LGD for credit provisioning purposes. Adaptability to dynamic way of working/short‑term deliverables. Preferred Qualifications Advanced or Fluent English. Experience with SOX controls within risk management environment. Seniority level Entry level Employment type Full‑time Job function Finance and Sales Referrals increase your chances of interviewing at Nubank by 2x. Sign in to set job alerts for “Credit Risk Specialist” roles. We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J-18808-Ljbffr