Model Risk Senior Specialist

Nubank•Virginia, VA

7d•Hybrid

About The Position

Nubank relies extensively on data, machine learning, and quantitative models to support strategy, product decisions, risk management, and customer experiences. The Global Model Risk Management team is part of the second line of defense and provides independent review, effective challenge, and oversight across the model lifecycle. Our work is not limited to identifying model weaknesses. We help the organization understand and manage model risk, improve decision quality, establish practical feedback loops, and evolve our governance as new techniques and applications emerge. We are looking for a Senior Model Risk Management & Validation Specialist to join our Global MRM team as a senior US-based model risk representative. In this role, you will independently assess and challenge models used across credit, fraud, operations, and other customer-impacting decisions. You will work with model developers, data scientists, product leaders, senior management, compliance, audit, and regulators to evaluate whether models are conceptually sound, appropriately implemented, fit for purpose, and effectively monitored. Although based in the US, this is a global role. You will bring knowledge of US regulatory expectations, industry practices, and model risk developments to help continuously evolve Nubank’s global MRM methodology, standards, governance, playbooks, and tools. You will also collaborate with colleagues across countries to promote consistent, scalable approaches that remain appropriate for local regulatory and business contexts. This is an opportunity to combine hands-on validation, US market expertise, and cross-border collaboration on global methodology in a fast-growing digital financial platform.

Requirements

Experience in model validation or model risk management (second line of defense) covering quantitative, statistical, machine-learning, or AI models, ideally in credit, fraud, or other high-impact decisioning processes.
Proven track record independently leading model validations end-to-end, exercising sound judgment with minimal oversight.
Strong data science and machine-learning fundamentals, including model performance, monitoring, and lifecycle management.
Strong programming and data skills, particularly in Python and SQL.
Analytical, autonomous, organized, and detail-oriented, with the ability to learn quickly and solve complex problems.
Excellent communication and interpersonal skills, with the ability to explain complex topics and provide constructive challenge to technical and non-technical stakeholders.
Practitioner mindset: hands-on experience using AI tools or agentic workflows to accelerate your own work (e.g., coding, documentation, research) — this role calls for someone who actively applies AI to modernize how MRM works, not only someone who evaluates AI risk in other teams’ models.
Working knowledge of US model risk regulatory expectations (e.g., Federal Reserve SR 11-7, SR 26-2) or comparable model risk frameworks.

Nice To Haves

Experience developing models (first line), in addition to validating them — gives a practitioner’s view of the modeler’s perspective.
Familiarity with US fair-lending practices (e.g., ECOA, Regulation B) and their application to credit and fraud model validation.
Experience translating regulatory expectations into practical validation standards, governance processes, controls, or documentation.
Experience evaluating AI/ML, generative-AI, or agentic-AI systems, including fairness, disparate impact, explainability, drift, or post-deployment monitoring.
Experience with Scala, Databricks, GitHub, AI-agent tools, or similar technologies.
Master’s degree, PhD, or relevant academic research in a quantitative discipline.
Experience working across countries, regulatory regimes, or globally distributed risk teams.
Experience supporting or participating in regulatory examinations (e.g., Federal Reserve, OCC, CFPB) related to model risk.
Familiarity with model tiering, model inventory management, and model risk rating frameworks.
Professional certifications such as FRM, CFA, or PRM.

Responsibilities

Lead or contribute to independent validation reviews of traditional statistical, quantitative, machine-learning, generative-AI, and other emerging models.
Lead independent validation and effective challenge across the model lifecycle — soundness, methodology, data, implementation, and performance — using benchmarking, back-testing, and sensitivity testing to surface risks and remediation actions.
Evaluate AI/ML-specific risks such as bias, fairness, explainability, and drift, and translate findings into practical, business-relevant recommendations.
Translate validation results into clear model-risk, business, and customer implications; define relevant performance and risk indicators; and prepare reports and presentations for technical and non-technical audiences.
Engage with senior management, internal audit, compliance, regulators, and other stakeholders to communicate independent opinions, findings, limitations, and recommendations.
Follow models into production to understand how they are implemented and used in real business processes, strengthening validation, monitoring, and feedback-loop practices.
Support the US CRO and senior leadership in representing Global MRM to US regulators, and bring US regulatory expectations, fair-lending practices, and market knowledge into ongoing enhancements to Nubank’s global methodology, standards, and governance.
Contribute to scalable global validation frameworks, playbooks, monitoring practices, and guidance for traditional, AI, generative-AI, and agentic systems while accounting for local regulatory and business requirements.
Act as a hands-on AI practitioner within MRM: use AI coding assistants, agentic workflows, and automation (Python, SQL, Scala, Databricks) to revamp and expedite validation, documentation, and monitoring processes, and share these practices across the Global MRM team.