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Manager Data Scientist Low Default Portfolios Models

BBVA

Job Location:

Madrid - Spain

Monthly Salary: Not provided by the employer

Posted: 4 June 2026 (30+ days ago)

Application Deadline: 1 September 2026

Vacancies: 1 Vacancy

Job Summary

Excited to grow your career

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 121000 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.