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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 GMRU COE is a hybrid team composed of data scientists and quantitative analysts dedicated to enhancing the risk management of BBVA’s trading activities by integrating advanced data technologies and machine learning models. The team’s primary role involves developing mathematical models and automated tools to support the Global Markets Risk Unit across several critical areas, including market risk (FRTB IMA), counterparty credit risk (IMM), stress testing, and valuation adjustments (AVAs). Beyond technical development, they serve as a strategic bridge between teams—such as Front Office, Internal Validation, and other COEs—to ensure that risk methodologies are robust, compliant with regulatory frameworks such as the ECB, and integrated into the bank’s data platforms like ADA.
About the job:
As part of this mission, we are looking for a technical profile to join a team currently undergoing a transformation of internal financial tools, which are presently based on Excel and rely on complex calculation logic supported by C++/.NET components through bindings such as SWIG. This work is part of a broader process of corporatization and industrialization of these tools, adapting them to corporate platforms such as ADA (AWS) and Aristeo, BBVA’s platform for deploying Docker images. The ideal candidate will have a strong foundation in Python, experience in systems integration through APIs and Docker, and comfort working with calculation logic. Knowledge of C++ and an affinity with, or interest in, the financial domain will be considered a plus.
Responsibilities:
Development and validation of quantitative models.
Efficient implementation in C++ and Python.
Industrialization of solutions (containerization, deployment, and maintenance).
Collaboration with business and technology teams.
Performance optimization and scalability improvements.
Qualifications:
Degree in Engineering, Mathematics, Physics, Quantitative Finance, or a related field.
At least 6 years of experience in a similar role.
Docker and application containerization.
Production deployment of models and systems (CI/CD, testing, monitoring).
C++ development (high performance, optimization).
Python programming (data analysis, prototyping, quantitative libraries).
Systems and API integration.
Practical knowledge of the following is a plus:
Financial modelling (pricing, risk, simulations, etc.).
Financial product models (derivatives, fixed income, equities, etc.).
Experience with tools such as Git, Docker, Artifactory, and Jenkins.
Experience with Kubernetes and cloud platforms (AWS, Azure, GCP).
Experience in banking or financial consulting environments.
Skills:
Client Orientation, Empathy, Ethics, Innovation, Proactive Thinking
