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.