Experteer Overview

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As a Simulation Engineer, you will advance BNP Paribas’ regulatory simulation engines by turning regulatory requirements into scalable Python services. You’ll ensure traceability, reproducibility, and CI/CD readiness, enabling robust cross-risk calculations. You work with cross-functional teams to integrate engines into production contexts and support model portfolio transformations.

This is a hands-on role at the intersection of tech, finance, and risk, offering meaningful impact in capital adequacy and regulatory compliance.

Compensaciones / Beneficios

- Design and develop Python-based simulation services and reusable analytical components
- Translate regulatory and business needs into scalable simulation methodologies and implementations
- Maintain and enhance simulation traceability, lineage and reproducibility across environments
- Implement testing, CI/CD pipelines and orchestration for industrial deployment




- Collaborate with IT/platform teams to integrate simulation engines with production calculation ecosystems
- Support collateral/guarantee simulation mechanisms and result reconciliation

Responsabilidades

- Master's degree in Computer/Data Engineering, Risk or Finance
- Minimum 4 years of experience in data/ML engineering, software development for credit-risk or regulatory models
- Strong Python development skills in analytical or simulation contexts
- Proficiency with version control, testing, documentation and code-review practices
- xugodme Good understanding of data pipelines, APIs and scalable execution environments (plus)
- Excellent English communication and ability to work with technical, analytical and regulatory stakeholders

Requisitos principales

- Comprehensive training programs
- International mobility
- Active diversity and inclusion initiatives
- Corporate volunteering program (1 Million Hours 2 Help)
- Adaptable compensation and hybrid work model (50% remote)
- 31 vacation days and generous leave policies

📌 Risk Simulation Engineer H/F (Madrid)
🏢 BNP Paribas
📍 Madrid