As a Senior Data Scientist within the AI Solutions discipline, you will contribute deep technical expertise to the evolution of BBVA’s financial foundation model, working across design, pre-training, adaptation, evaluation, and large-scale training infrastructure to help build reliable, responsible, and reusable AI capabilities that can transform how data is understood and applied across the organisation.

About the job:

BBVA is developing state-of-the-art capabilities designed around the specific characteristics of financial services. As part of this ambition, we are exploring foundation models that can learn from the different forms of information generated across banking, including transactional sequences, product interactions, operational events…

We are looking for a Data Scientist with strong expertise in the design, training, adaptation, and evaluation of large-scale AI models. This is a senior position for someone who can address difficult technical problems, contribute specialist knowledge, and take end-to-end ownership of important areas of the foundation model lifecycle.

The role will contribute to decisions involving model architectures, training objectives, data preparation, distributed training, fine-tuning, evaluation, and production deployment. The successful candidate will work closely with other engineers, researchers, data specialists, platform teams, and business stakeholders, using their expertise to improve technical decisions and raise the overall quality of the initiative.

What Makes This Role Different

This is not a conventional position, it is an opportunity to contribute to the creation of a new AI capability that could influence how data and intelligence are used across BBVA.

The Foundation Models initiative aims to establish general purpose components that can support multiple areas of the organisation. Its potential extends across credit, recommenders, LTV, financial crime, etc.

The initiative could also change how AI solutions are developed within BBVA. Instead of building separate models and data pipelines for every problem, teams may be able to use shared representations and adaptable model capabilities as a common starting point. Your expertise will contribute to choices involving project end-2-end.

Key Responsibilities

Foundation Model Development

• Define approaches for filtering, sampling, balancing, sequencing, and combining data from different banking domains.

• Design tokenisation, feature representation, masking, and sequence-generation strategies suited to financial information.

• Address the characteristics of sensitive, sparse, imbalanced, and temporally ordered data.

• Detect and mitigate potential issues such as information leakage, historical bias, missing values, geographic variation, and changes in behaviour over time.

• Design, implement, and train models adapted to financial and banking information.

• Contribute expert judgement to the selection of architectures, learning objectives, training strategies, and customer and event representations.

• Work with structured and unstructured sources, including text, tabular datasets, transactions, event sequences.

• Investigate and resolve problems related to convergence, training stability, data quality, memory usage, and computational performance.

• Design reproducible experiments and evaluate technical alternatives using clear evidence and well-defined criteria.

• Develop and compare adaptation strategies, including continued pre-training, supervised fine-tuning, parameter-efficient methods, and task-specific modelling approaches, selecting the most appropriate technique for each financial use case.

Training Infrastructure and scale

• Work with platform and engineering teams to define, evolve, and use the technical environments required for reliable large-scale model training.

• Improve accelerator utilisation through profiling, parallelism, scheduling, memory optimisation, and performance tuning.

• Monitor long-running experiments and implement mechanisms that improve their reliability, recoverability, and auditability.

• Evaluate model performance across countries, customer groups, products, time periods, and changing economic conditions.

• Support the transition of successful experiments into robust production implementations.

Ways of Working

• Work closely with researchers, engineers, data scientists, architects, product teams, and control functions across BBVA.

• Share reusable methods, tools, documentation, and engineering practices with the wider AI and data community.

• Explain model behaviour, technical trade-offs, risks, and limitations to both technical and non-technical stakeholders.

• Help teams understand where foundation models can provide value and where alternative approaches may be more suitable.

• Participate in internal technical forums and, where appropriate, external research or industry activities.

• Ensure consistent application of WoW (Ways of Working) guidelines for models and engines across all products.

Required Qualifications

Experience

• 5+ years of experience in leadership roles across technology, analytics, or AI-driven organisations.

• Experience with distributed compute and orchestration technologies.

• Demonstrated ability to independently own complex technical problems from investigation through implementation.

• Substantial hands-on experience designing, training, or adapting large-scale models in research or production settings.

• Preferred familiarity with financial datasets such as payments, transactions, credit information, risk indicators, regulatory documents, or financial crime data.

• Practical knowledge of sequential modelling, GNN, LLM pre-training, including architecture selection, data pipelines, distributed execution, optimisation, and training stability.

Skills

• Advanced programming ability in Python and strong software engineering practices.

• Deep understanding of modern machine learning and deep learning techniques.

• Strong practical experience with PyTorch or an equivalent deep learning framework.

• Detailed knowledge of transformer architectures, representation learning, optimisation, and large-scale model training.

• Ability to implement and troubleshoot distributed training across multiple accelerators or compute nodes.

• Familiarity with parallel training, mixed precision, checkpointing, experiment tracking, memory optimisation, and performance profiling.

• Understanding of model evaluation beyond aggregate accuracy, including robustness, fairness, calibration, explainability, and operational reliability.

• Ability to work with sensitive, complex, imbalanced, and temporally structured datasets.

• Desired experience with full fine-tuning, supervised fine-tuning, LoRA, or other parameter-efficient adaptation methods.

Education

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, Physics, Engineering, or another relevant quantitative discipline.

• A PhD in a related field would be an advantage but is not required when the candidate can demonstrate equivalent technical expertise and practical experience.

Why Join Us?

This is a unique opportunity to shape how AI products are built, governed, and delivered at scale within a global financial institution. You will work at the intersection of technology, governance, and innovation, driving initiatives that directly impact millions of users and position BBVA as a leader in AI development.

Skills:

Client Orientation, Empathy, Ethics, Innovation, Proactive Thinking