BBVA (Banco Bilbao Vizcaya Argentaria) is a leading global financial services group and one of the largest financial institutions in the world, with a significant presence in Spain, Mexico, South America, and Turkey. The bank is widely recognized for its pioneering digital transformation, consistently ranking as a global leader in mobile banking and fintech innovation while managing over 120,000 employees worldwide. Headquartered in Madrid, BBVA is a major employer for international professionals, particularly within its Corporate & Investment Banking (CIB) and Engineering & Data divisions. The company operates out of 'Ciudad BBVA,' a massive, modern campus designed for agile collaboration, and offers a multicultural environment where English is the primary language for many global and technical functions.

BBVA was formed in 1999 through the merger of Banco Bilbao Vizcaya and Argentaria, though its roots date back to 1857 with the founding of Banco de Bilbao. It has evolved from a local Spanish bank into a global financial powerhouse and a digital banking pioneer.

Manager Data Scientist - Low Default Portfolios Models

BBVA

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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 CIB Credit COE is responsible for the development, implementation and maintenance of advanced credit risk models, with a particular focus on Low Default Portfolios (LDP). The area combines quantitative modelling, advanced analytics and artificial intelligence capabilities to enhance risk measurement, decision-making and capital optimization. It also drives the generation of risk intelligence and the automation of processes through AI-based solutions, working closely with Risk, Business and Technology teams while ensuring alignment with regulatory frameworks (IRB).

About the job:

We are seeking an experienced Manager Data Scientist to play a key role in the development, maintenance and enhancement of credit risk models for Low Default Portfolios (LDP). The position combines strong quantitative expertise with project ownership, acting as a technical lead in the design of IRB-compliant models and methodologies supporting capital, provisioning and risk management decisions.

The role requires close collaboration with business, risk, validation and technology teams, as well as active participation in model governance and regulatory processes.

Responsibilities

Lead the development and maintenance of credit risk models and parameters (PD, LGD, CCF) for Low Default Portfolios across different asset classes.
Design and implement methodologies for model estimation, calibration and monitoring, ensuring robustness and regulatory compliance.
Perform advanced data analysis, feature engineering and segmentation studies to support model performance and interpretability.
Coordinate the integration of new internal and external data sources to enhance existing modelling frameworks.
Act as a technical reference for model governance, including documentation, model reviews, validation processes and remediation actions.
Support interactions with internal validation, audit and supervisory authorities (e.g. IMIs), providing quantitative analysis and methodological explanations.
Collaborate with technology teams to ensure efficient and robust model implementation in production environments.
Provide technical guidance and mentoring to junior team members, contributing to their professional development.

Qualifications

> 5 years of experience in quantitative or analytical roles, preferably within credit risk modelling or risk management in financial institutions.
Strong experience in Low Default Portfolio modelling.
Solid knowledge of IRB regulatory frameworks and credit risk modelling requirements.
Proven experience delivering end-to-end modelling projects, from data analysis and methodology design to implementation and monitoring.
Advanced proficiency in Python (or similar tools) for statistical modelling and data processing.
Ability to translate complex quantitative results into clear insights for business and senior stakeholders.
Experience working in multidisciplinary environments, coordinating with business, IT and risk teams.
Experience in database data quality (DQ), and expertise in the new default definition is a plus.
Fluent in English.

Skills:

Client Orientation, Empathy, Ethics, Innovation, Proactive Thinking