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.

Work setup: Hybrid 2-3 times/week — office requirement is 2 days per week at the office.
Recruitment process may involve artificial intelligence–enabled tools, such as automated interview transcription and analysis, to support evaluation.
Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.

Nubank is hiring a Credit Risk Specialist