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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.

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The Global Markets Risk Unit (GMRU) area is responsible for the measurement, control, and management of market and counterparty credit risks, valuation adjustments (XVA), calculation of economic capital across BBVA’s global market positions, as well as fair value valuation, independent price verification, and quality assessment of Front Office quantitative models. All these activities are performed in accordance with applicable international regulatory frameworks and sound risk management practices.

In close coordination with quantitative analytics teams located in Front Office and other risk departments, the GMRU Advanced Analytics Team develops the quantitative methodologies and tools required to execute GMRU core processes and leads key projects related to regulatory change. The team brings together quantitative analysts and data scientists to drive innovation in risk modeling.

About the job:

About you

You hold a strong quantitative and analytical background with a keen interest in mathematical modeling within practical financial environments. You are passionate about applying data science, quantitative finance, and machine learning to financial risk management. You enjoy programming, building scalable risk software, and working in cross-functional environments. You possess excellent communication skills to interact effectively with diverse technical and executive stakeholders, and you excel as a collaborative team player.

As a Data Scientist Manager , your primary responsibilities will include:

• Model Development & Methodology: Design, develop, and implement advanced mathematical models, data-driven methodologies, and quantitative tools for measuring and managing market and counterparty credit risks associated with Global Markets products.

• Risk Scope & Metrics: Drive quantitative initiatives covering market risk metrics (VaR, Stressed VaR, FRTB framework), counterparty credit risk measurement (IMM, PFE), valuation adjustments (XVA), and economic and regulatory capital calculations.

• Stakeholder Collaboration: Partner closely with Risk Managers within the Global Risk Management Unit to ensure alignment with regulatory frameworks (ECB, EBA, EBA/FRTB) and sound risk practices. Collaborate with Front Office quantitative teams to validate and align valuation models.

• Software Architecture & Testing: Enforce code development policies, software architecture standards, and rigorous testing frameworks (CI/CD, unit testing) to ensure robust, maintainable, and reusable codebase across teams.

• Leadership & Project Management: Lead technical workstreams within regulatory transformation projects, mentoring junior quantitative analysts and data scientists.

Qualifications & Requirements

Education:

• Required: University Degree (Bachelor's or Master's) in Mathematics, Physics, Quantitative Engineering, Actuarial Sciences, Quantitative Economics, or a related STEM field.

• Highly Valued: Master’s degree or Ph.D. in Quantitative Finance, Financial Engineering, Artificial Intelligence, Big Data, or Applied Mathematics.

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Professional Experience:

• Minimum 6+ years of professional experience in quantitative risk analysis, financial engineering, or data science applied to banking, investment banking, or capital markets.

• Proven track record in market risk modeling, counterparty credit risk, XVA, or pricing derivatives within investment banking / corporate banking units.

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Key Skills:

• Financial & Risk Expertise: Solid understanding of financial markets, derivative pricing (fixed income, credit, inflation), risk management concepts (market and counterparty credit risk related), and regulatory risk frameworks (FRTB, IMM).

• Programming & Tech Stack: Advanced proficiency in at least one object-oriented or data programming language: Python (NumPy, SciPy, Pandas, PyTorch/TensorFlow), C++, or C#.

• Data Science & ML: Practical experience with machine learning techniques applied to quantitative finance (e.g., anomaly detection, calibration optimization).

• Software Engineering: Familiarity with Git version control, continuous integration/continuous delivery (CI/CD) pipelines and containerization (Docker).

Languages:

• English: B2 (Advanced/Fluent) or higher (written and spoken), as this position operates in a global environment with international stakeholders.

Skills: