Credit 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

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

What We're Looking For Someone Who Has

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.

Work Setup

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.

Our Benefits

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.