MANAGER GRM DATA & ANALYTICS - WHOLESALE CREDIT RISK MODELS & PARAMETERS | MADRID, ES

Manager GRM Data & Analytics - Wholesale Credit Risk Models & Parameters 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 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers and designers.
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La publicación estará activa hasta el 29 a las h.
The Global Risk Management (GRM) division plays a crucial role in ensuring sustainable growth and value creation through risk-adjusted profitability strategies.
Our mission is to enhance capital efficiency and strengthen risk management by leveraging data, advanced analytics, artificial intelligence and digitization frameworks.
About the job.
Role Overview As Manager I for Wholesale Credit Risk Models & Parameters, you will be responsible for leading and coordinating the development, monitoring, and enhancement of wholesale credit risk models, including rank ordering models (Rating, EWS, Limit Advisor) and parameter models (PD, LGD, EAD) under IRB, IFRS9, and Economic Capital frameworks.
You will work closely with stakeholders across Holding and local units to ensure regulatory compliance, model excellence, and business alignment.
You will join a multicultural and high-performing team in the GRM Analytics Center of Expertise, and contribute to BBVA's transformation towards a data-driven risk function.
Your work will directly impact BBVA's risk management strategy, ensuring compliance with BIS, EBA, ECB, CRR3, IFRS9, and other supervisory expectations while optimizing risk-adjusted profitability.
Main Functions • Develop and maintain credit risk parameter models (PD, LGD, EAD) and rank ordering models (Rating, EWS, Limit Advisor) for wholesale portfolios.
• Ensure full alignment with regulatory requirements (IRB, IFRS9, CRR3, BIS, EBA, ECB), governance frameworks, and internal validation standards.
• Lead the end-to-end model lifecycle, including design, implementation, documentation, and backtesting, in coordination with local and Holding teams.
• Collaborate with Data and Engineering teams to address data quality and infrastructure needs to support model development and monitoring.
• Drive the integration of advanced analytics techniques (e.
., Machine Learning, alternative data sources) and support the transition to modern, cloud-based development environments.
• Contribute to the definition of group-wide modelling standards, ensuring methodological consistency and best practices.
• Provide technical guidance and mentorship to junior team members, fostering a culture of collaboration, innovation, and continuous improvement.
Minimum Requirements • University degree in a quantitative field (Mathematics, Statistics, Physics, Engineering, Economics, Actuarial Science).
A Master's degree will be highly valued.
• At least 8 - 9 years of experience in credit risk model development or validation, particularly under IRB and IFRS9 frameworks.
• Strong knowledge of regulatory expectations, supervisory guidelines, and internal governance related to credit risk modelling.
• Proven expertise in parameter estimation techniques and exposure modelling.
• Proficiency in analytical tools and programming languages (Python, SQL, R).
• Experience in data mining, data visualization, and Big Data environments.
• Advanced level of English (C1 or above), both written and spoken.
• Experience working in multinational and cross-functional environments.
Desired Requirements • Familiarity with AI/ML techniques applied to credit risk modelling.
• Exposure to cloud technologies (e.
., AWS) and modern software development workflows.
• Strategic thinking, strong project management capabilities, and ability to operate effectively under pressure.
• Excellent communication skills with the ability to translate technical concepts to non-technical audiences.
Skills.
Customer Targeting, Empathy, Ethics, Innovation, Proactive Thinking