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BBVA is a global company with more than 160 years of history that operates in more than 25 countries where we serve more than 80 million customers. We are more than 121,000 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers and designers.
Learn more about the area:
The Model Risk Management team develops risk measurement methodologies for the entire BBVA Group. It also makes the oversight process of all the GRM models ensuring the consistency and the strategic alignment of the model landscape in GRM. MRM team combines data science, econometrics, and advanced analytics to build tools that support strategic decision-making within Global Risk Management.
Within this area, the Market, Structural, ESG & ICAAP Team focuses on developing quantitative methodologies that integrate physical risks (floods, extreme temperatures, droughts, etc.) and transition risks (regulatory, technological, or market changes) into traditional credit and capital risk metrics. Supporting ESG risk initiatives and alignment with European regulatory requirements (EBA, ECB, NGFS, etc.). As well as making the oversight of the corresponding models developed in the different GRM Analytics teams.
About the job:
As an MRM Standard Setter , you will work within the Model Risk Management area, responsible for defining the methodological guidelines and standards for the development and integration of climate risk models into economic capital, provisioning, and stress testing frameworks across the BBVA Group. The actual development and implementation of these models are conducted by separate GRM Analytics teams.
This position offers the opportunity to develop advanced machine learning and structural modeling techniques to quantify the financial impacts of physical and transition climate risks within a global banking environment. This testing process is a key part of the development of internal model development standards.
You will contribute to the design of the methodologies to develop (and backtest) models that link climate projections with financial risk metrics (PD, LGD, EAD, etc.), ensuring methodological robustness and alignment with regulatory and sustainability frameworks.
This role combines quantitative excellence, financial and climate risk understanding, and close collaboration in a multidisciplinary environment.
Main responsibilities
Methodologies development and enhancement: Design, develop, and maintain quantitative methodologies that incorporate climate risk into provisioning, stress testing, and economic capital frameworks. These methodologies should be included in the Risk Model Development Guidelines.
Quantitative analysis : Apply statistical, econometric, and machine learning techniques to define methodologies to estimate financial impacts derived from climate scenarios.
Model governance : Prepare detailed Model Development Guidelines, as well as backtest Guidelines. Monitor the resolution of the different model recommendations.
Advocacy : Participate in international Climate Risk related working groups.
Research: Be up to date in the most recent Climate risk related regulation as well as recent papers on climate risk modelling
Collaboration and Oversight : Work closely with credit risk, sustainability, and regulatory teams to ensure the effective integration of climate risk into existing risk management frameworks. Make the oversight of the local GRM Analytics teams to ensure the strategic alignment of the models developed.
Qualifications and requirements
Education : Degree in Physics, Mathematics, Engineering, or another quantitative discipline.
Master’s in Machine Learning, Data Science, or similar is required. Additionally the FRM/SCR/RAI certifications are valuable.
Experience : at least 7 years of experience in quantitative model development or analytics, preferably within the banking or financial sector. Or 3 years of experience developing developing Climate Risk related models.
Technical skills are a must :
Proficiency in Python/SQL/PySpark and data management tools AWS (Athena/Sagemaker/Engines)
Knowledge of credit risk modeling (PD, LGD, EAD) and/or stress testing.
Experience applying machine learning techniques to risk or climate modeling.
Knowledge of climate scenario frameworks (NGFS, IPCC) and regulatory guidance (EBA, ECB) is a plus.
Soft skills:
Strong analytical mindset and attention to detail.
Excellent communication skills in Spanish and fluent in English (B2).
Ability to collaborate effectively in international and multidisciplinary teams.
Core competencies
Quantitative rigor and methodological discipline
Scientific curiosity and innovation mindset
Ability to translate climate metrics into financial impacts
Collaboration and cross-team communication
Awareness of regulatory and ethical modeling standards
Skills:
Backtesting, Capital Models, Credit Risks, Disciplined, Machine Learning (ML), Python (Programming Language), Regulatory capital (Risk Management vision), Risk Models, Statistics, Supervision, Sustainability, Sustainability Strategy, Sustainable Business, Transitional risk, Value at Risk (VaR)
