Model Risk Specialist

About Nu

Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services.

Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns.

Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks.

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About the Role

At Nubank we heavily rely on Data, Machine Learning, and increasingly on Generative and Agentic AI to drive our strategy and deliver the best experience and products to our customers. The Model Risk team plays a crucial role in ensuring the risks associated with our models and AI systems are understood and under control. We are now building a dedicated AI Risk Management capability to address the emerging risks of advanced AI — including LLM-powered and autonomous agentic systems — with a focus on AI quality, model and agent behavior, and the platform controls that keep these systems safe and reliable across internal and customer-facing use cases.

This is an individual contributor role, you will both review and assess what first-line teams build, and actively develop tools, playbooks, and analyses to mature our risk practices. You will focus on the infrastructure and data risks that surround model development and deployment: feature engineering and feature stores, MLOps pipelines, model monitoring, deployment platforms, and data governance practices. You will work closely with model and data platform teams to identify, assess, and report risks independently, bringing a second-line perspective without losing technical depth.

Responsibilities

Infrastructure & Data Risk Assessment

• Conduct independent reviews of the data and infrastructure environments used for developing, deploying, and monitoring AI/machine learning models, assessing reliability, stability, and fitness for purpose.
• Evaluate risks across the model lifecycle infrastructure: feature engineering pipelines, feature stores, CI/CD for models, deployment platforms, and model monitoring systems.
• Assess data governance practices, including data quality, lineage, access controls and identify gaps that could materially impact model behavior or risk.
• Identify and escalate risks or control gaps proactively across all stages of the model and data platform lifecycle.

Controls & Governance

• Help establish and enhance specific controls and validation practices for data and infrastructure used in model development and deployment.
• Review and challenge first-line processes, procedures, and controls against internal policies, industry frameworks, and regulatory expectations.
• Partner with model and data platform teams to define and monitor Key Risk Indicators (KRIs) for infrastructure and data risk.
• Contribute to the evolution of Nubank's model governance framework with an infrastructure and data lens.

Tooling & Playbooks

• Develop and improve tools, analyses, and playbooks specific to infrastructure and data risk management.
• Build reporting and monitoring solutions that provide clear, continuous visibility into the health of model infrastructure and data environments.
• Support internal audit and regulatory inquiries with well-documented, traceable, and reproducible risk assessments.

Stakeholder Engagement

• Discuss and report infrastructure and data risk status, findings, and independent opinions with stakeholders across the organization, including senior managers.
• Collaborate with model teams, data platform engineers, and governance partners to drive risk-aware design decisions without slowing responsible innovation.
• Work in a multicultural, diverse, and highly skilled environment.

Qualifications

• Bachelor's or master's degree in computer science, data science, statistics, mathematics, engineering, or a related field.
• Strong programming skills: Proficiency in Python and SQL; experience with Spark/Scala is a plus.
• ML infrastructure knowledge: Hands-on familiarity with MLOps practices, CI/CD pipelines for models, feature stores, and cloud-based ML platforms (AWS, GCP, or Azure).
• Data governance: Understanding of Data Mesh architecture and principles, data lineage, data quality frameworks, and feature governance practices.
• Model monitoring: Experience with model and data monitoring concepts, including drift detection, pipeline stability, and performance degradation tracking.
• Good written and verbal communication skills in English
• Understanding of risk management principles, control design, and governance frameworks (e.g. ERM, COSO); experience in a 2nd or 3rd line of defense is a strong plus.
• Prior experience in model risk management, model validation, or a data/ML engineering role transitioning into risk (nice to have)
• Familiarity with model risk frameworks and regulatory expectations (e.g., SR 11-7 / SR 26-2 / OCC 2011-12, NIST AI RMF)-(nice to have)

Location & Work Model

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.

This position is based in Sao Paulo, Brazil.

Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare – Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage – Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
• Relocation Assistance Package, if applicable

Our recruitment process may involve the use of artificial intelligence–enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.