Model Risk Senior Specialist
NubankBrazil, Sao PauloPosted 20 March 2026
Tech Stack
Job Description
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 one of the world’s largest digital banking platforms and technology-leading companies. 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.nubank.com.br
About the team:
At Nubank we heavily rely on data, machine learning, and AI to drive our strategy and provide the best experience and products to our customers.The Model Risk team plays a crucial role in ensuring Nubank relies on world-class models that meet business and customer needs, and have associated risks under control. We act by providing effective review challenge and identifying opportunities to improve our models. We also act by implementing governance and validation standards to control the risk of our models, as well as by establishing feedback loops to constantly improve them.
The position: This is a senior individual contributor role that will require experience in machine learning and analytics, along with knowledge of Expected Credit Loss and its parameters (PD, EAD, LGD).
You will be responsible for
Conduct independent reviews of quantitative models based on financial, statistical, and machine learning techniques, with a focus on credit risk provisions and capital
Provide effective challenge to models and modelling processes, identify risks and engage with other teams to strengthen our decision-making tools and capabilities.
Develop playbooks and toolkits (e.g., Python, SQL, Scala) to optimize model reviews, ongoing monitoring, and assess the impact of models on decision-making.
Contribute to the construction of Nubank's Model Risk Management and Model Review processes with autonomy and creativity.
Discuss and report model risk status and receive independent opinions from various stakeholders, including senior managers and regulators.
Be exposed to models from different countries and regulatory requirements.
Have a strong understanding of relevant local and international regulations and accounting standards concerning credit provisions, including BACEN 4966 and IFRS9.
Ensure the team maintains technical excellence and actively contributes to establishing model risk controls and processes that strengthen compliance with regulations.
Mentor junior staff on best practices for Model Oversight and Validation.
Work in a multicultural, diverse and highly skilled environment.
We are looking for a person who has
Experience developing or validating quantitative and/or machine learning models, with a focus on credit provisions and capital requirements models.
Interest in reviewing complex machine learning and other types of models to identify risks and propose enhancements.
Data Science skills, strong knowledge of machine learning tools and techniques.
Strong programming skills, desirable previous experience with Python, SQL, GitHub.
Ability to learn fast, strong analytical, and problem-solving skills.
Organized and detail-oriented, without losing track of the big picture.
English language proficiency.
Experience in relevant local and international regulations and accounting standards related to credit provisions and capital, such as BACEN 4966, IFRS9, ICAAP.
Benefits:
Health, dental, and life insurance
Meal allowance
Transportation assistance
30 days of paid vacation
Chance of earning equity at Nubank
Parking partnership - discounted parking in our office
Free bike parking with showers available
NuCare - Our m ... (truncated, view full listing at source)
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