About us
Santander Brasil has been present in Brazil since 1982, is the third-largest private bank in the country by assets, and the only international bank with national retail scale. It is a digital bank with people.
Job description
By joining the Regulatory Models New Generation team at Santander, you will work in the area responsible for developing, implementing, monitoring and evolving the organization's regulatory models for credit risk provisions and capital. The area's main objectives are to ensure regulatory adherence, technical excellence of the models, precision of risk estimates, and continuous evolution of the Bank's analytical and technological capabilities, using data-driven solutions, automation and artificial intelligence.
We have an opening for you to become a Senior Modeling Technician, where you will be positioned as a Specialist, being one of the main technical references in the construction, implementation and support of the regulatory models of the Credit Risk area.
Here, your role will be:
Working hands-on in the development, calibration, implementation and monitoring of provisioning models (IFRS 9 and CMN Resolution 4.966) and regulatory capital (IRB), ensuring methodological robustness, computational efficiency and regulatory adherence.
You will be responsible for developing scalable solutions in Python and PySpark, automating processes using Artificial Intelligence and Machine Learning, supporting validations, audits and regulatory demands, as well as contributing to the evolution of best quantitative modeling practices in the organization.
Main responsibilities
•Develop and calibrate ECL models (PD, LGD and EAD) for IFRS 9;
•Implement provisioning models compliant with CMN Resolution 4.966;
•Support the development and evolution of IRB models;
•Perform monitoring, backtesting, validation and stress testing of models;
•Process and analyze large volumes of data;
•Develop automated end-to-end pipelines for model execution;
•Implement solutions in Python, PySpark and Big Data environments;
•Apply Artificial Intelligence, AutoML and GenAI for automation and productivity gains;
•Investigate deviations, anomalies and opportunities for methodological improvement;
•Prepare technical documentation and materials for audits, validations and regulators;
•Support the technical development of less experienced professionals.
Required experience
•Deep experience in Credit Risk or Quantitative Modeling areas; and extensive experience with IFRS 9 topics and Python applied to risk modeling;
•Experience in financial institutions;
•Proven experience in implementing and supporting models in production;
•Knowledge of validation, audit and model governance processes.
Education
•Completed degree in Mathematics, Statistics, Physics, Engineering, Economics, Computer Science or related fields;
•Postgraduate studies, MBA or Master's in quantitative areas will be considered a differential.
Desirable certifications
•FRM (Financial Risk Manager);
•PRM (Professional Risk Manager);
•Certifications in Data Science or Machine Learning;
•Cloud certifications (AWS, Azure or GCP).
Languages
•Advanced English for reading technical documentation and interacting with global forums;
•Spanish will be considered a differential.
Competencies
Hard Skills
Risk Modeling and Provisions:
•IFRS 9;
•CMN Resolution 4.966 (BRGAAP);
•IRB;
•PD, LGD, EAD;
•Expected Credit Loss (ECL);
•Staging and SICR;
•RWA;
•Stress Testing and Backtesting.
Programming and Data Engineering:
•Python;
•PySpark;
•SQL;
•Pandas and NumPy;
•Git and GitFlow;
•Clean Code;
•Design Patterns;
•Pytest.
Statistics and Machine Learning:
•Logistic Regression;
•GLM and GAM;
•Survival Analysis;
•XGBoost;
•LightGBM;
•Random Forest;
•Model calibration;
•AUC, Gini and KS metrics.
Artificial Intelligence and Automation:
•ChatGPT, Claude and GitHub Copilot;
•AutoML;
•Optuna and Hyperopt;
•SHAP and LIME;
•Streamlit and Dash;
•Airflow and Prefect;
•Generative AI-based process automation;
•Development of agents and productivity tools.
Big Data and Cloud:
•Apache Spark;
•Databricks;
•Delta Lake;
•Parquet;
•AWS and Azure;
•S3 and EMR.
Soft Skills
•Critical and analytical thinking;
•Strong data orientation and evidence-based decision making;
•AI-driven mindset and focus on innovation;
•Hands-on profile and strong execution capacity;
•Results and quality orientation;
•Attention to detail and technical rigor;
•Ability to communicate with technical and executive audiences;
•Collaboration and cross-functional work;
•Knowledge sharing and technical mentoring;
•Proactivity in identifying and resolving complex problems.
