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BBVA es una compañía global con más de 160 años de historia que opera en más de 25 países donde damos servicio a más de 80 millones de clientes.
Somos más de **
* profesionales trabajando en equipos multidisciplinares con perfiles tan diversos como financieros, expertos legales, científicos de datos, desarrolladores, ingenieros y diseñadores.
At BBVA AI Factory...
BBVA AI Factory operates as a integral hub within the Data area of BBVA, with development centers in Spain, Mexico, and Turkey.
Our mission is to build complete, end-to-end data products that solve BBVA's business needs by working closely with business units to transform strategic priorities into actionable, data-driven solutions.
Some of our recent projects include:Mercury Library, an in-house AI framework now available to the entire data community, aimed at boosting collaboration and accelerating AI solution development.
( machine learning pipeline designed to enhance early debt recovery by predicting default risk and optimizing collection strategies ( daily life embeddings to drive deeper personalization in customer interactions and improve service recommendations.
( At BBVA AI Factory, innovation isn't just a goal?it's a continuous journey.
Sobre el puesto At BBVA AI Factory, we're seeking an Data Scientist to join our dynamic team in Madrid, specializing in the application of Generative AI and Large Language Models (LLMs).
Whether it's developing explainable algorithms, building Machine Learning pipelines to improve debt recovery, or leveraging LLMs for enhanced customer experiences, we focus on creating real-world impact.
As the technical reference for Generative AI within your domain, you will lead the data science approach for end-to-end AI products that enhance key banking processes.




Working with product managers, data engineers, ML engineers and other specialist teams, you will guide the development of robust LLM-based solutions and help develop a new AI-supported customer relationship model that benefits both customers and managers.
Key Job Responsibilities Technical Leadership: Act as the technical reference for Data Science and Generative AI within the domain, defining the analytical and modelling approach for complex business problems.
Lead Analytical Initiatives: Lead high-impact analytical initiatives from problem framing to production deployment, defining objectives, success metrics, experimentation plans and model performance criteria.
Develop and optimize predictive and generative models using statistical, machine learning and LLM techniques.
LLM Solution Design: Design the data science components of LLM solutions, including data preparation, knowledge-base design, retrieval strategies, prompt engineering, model selection and evaluation methodologies.
Data and Model Lifecycle: Ensure sound practices across the data and model lifecycle, from dataset definition and exploratory analysis to testing, monitoring and model improvement.
Cross-Functional Collaboration: Work closely with product managers, data engineers, ML engineers, platform teams and specialist control functions to deliver production-ready data products.
Technical Decision-Making:



Make and document key data science trade-offs, such as model selection, evaluation thresholds, data requirements and experimentation design.
Technical Mentorship: Mentor data scientists and contribute through technical reviews, knowledge sharing and guidance on analytical and modelling best practices.
Best Practices Compliance: Ensure that data science deliverables follow Advanced Analytics governance standards and agreed development practices.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.

Experience:
8+ years of experience as a Data Scientist or Machine Learning practitioner, including substantial experience delivering machine learning and LLM solutions to production.
Programming & ML Frameworks: Deep expertise in Python, statistical modelling and machine learning, with strong proficiency in Pandas, NumPy and scikit-learn, plus hands-on experience with PySpark, TensorFlow, PyTorch and HuggingFace Transformers.
Data Lifecycle Management: Proven experience across the end-to-end lifecycle of ML and LLM models, from dataset definition and EDA to experimentation, testing, deployment, monitoring and iterative improvement.
Technical Leadership: Proven ability to define data science approaches for complex problems, make sound modelling decisions, conduct technical reviews and mentor less experienced practitioners.
Strong understanding of LLM limitations and model risks, including hallucination, bias, performance variability and limitations arising from data quality.
Applied Machine Learning: Deep knowledge of a broad set of machine learning techniques applied to solve complex business problems, including A/B testing for model performance.
Ethical AI and Responsible Data

📌 Senior Expert Data Scientist Genai (Madrid)
🏢 Bbva
📍 Madrid