Credit Risk Specialist

Nu serves more than 140 million customers, guided by a mission to fight complexity and empower people. The company has been leading an industry transformation through innovative products and human-centered services.

Proprietary technology and data at scale power Nu’s digital platform, built to promote financial access, advancement, and transparency. Its business model thrives on customer love and lower costs, feeding a flywheel of growth and profitability.

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As a Credit Risk Specialist, you'll own how the provisioning process actually runs. Your focus is the reliability, scalability, and observability of the credit-risk data and model pipeline - the orchestration, data-quality checks, and platform capabilities that let our risk models run correctly every cycle, supporting model monitoring, use and process controls.

You bring a credit-risk analyst's judgment to the numbers, but your day-to-day is making the pipeline robust, automated, and self-monitoring. This is a role for someone who leans over data - strong on data science / ML pipeline maintenance, comfortable in code, and motivated by turning processes into dependable, low-touch systems.

You’ll be Responsible for

• Own pipeline reliability - build, maintain, and harden the data pipelines that feed the ECL calculation, so each cycle runs low-touch and fully reproducible.
• Maintain the model-scoring pipeline - operationalize and productionize risk models (e.g. PD/EAD/LGD scoring runs), keeping the path from model hand-off to production standardized and repeatable.
• Manage upstream dependencies - make the pipeline resilient to external data changes; detect breakage early.
• Build data-quality and monitoring alerts - instrument the pipeline so data and process issues surface automatically before they reach the numbers.
• Advance the platform roadmap - contribute deliberate, roadmapped improvements so the platform stays ahead of new demands.
• Collaborate across data engineering, modeling, and finance - partner with the teams whose data you consume and whose reports you feed.
• Support controls and audit - help capture control and data-quality evidence continuously from source systems rather than by hand each cycle

• Bachelor's degree in Engineering, Computer Science, Data Science, Statistics, Math, Physics, Economics, or a related quantitative field.
• Strong coding and data engineering skills - SQL and Python, with hands-on experience building and maintaining data / ML pipelines.
• Experience keeping data science or machine-learning pipelines in production - scheduling, monitoring, data-quality checks, and debugging failures.
• Analytical, problem-solving mindset, genuinely motivated to work with data and learn from it.
• Working understanding of credit risk concepts and how portfolio data maps to risk metrics.
• Adaptability to a dynamic way of working.

Location

Sao Paulo, Brazil

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.

Office requirement

2 days per week at the office.

BRAZIL JOBS:

• 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.

To maintain a consistent and fair process for every candidate, Nu does not provide individualized technical feedback. See how our policy works here