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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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At BBVA AI Factory...

BBVA AI Factory operates as a global 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. [https://www.bbvaaifactory.com/category/mercury/]

• A machine learning pipeline designed to enhance early debt recovery by predicting default risk and optimizing collection strategies [https://www.bbvaaifactory.com/a-machine-learning-pipeline-for-early-debt-recovery-executive-summary/]

• Applying daily life embeddings to drive deeper personalization in customer interactions and improve service recommendations. [https://www.bbvaaifactory.com/behind-daily-life-embeddings-in-action/]

• Utilizing conformal prediction to provide reliable uncertainty estimates and enhance the confidence in AI model predictions [https://www.bbvaaifactory.com/conformal-prediction-an-introduction-to-measuring-uncertainty/]

• Building algorithmic explainability frameworks to ensure transparency and foster trust in our AI systems. [https://www.bbvaaifactory.com/algorithmic-explainability-how-do-we-apply-it-in-bbva-ai-factory/ ]

At BBVA AI Factory, innovation isn't just a goal-it's a continuous journey.

About the job:

At BBVA AI Factory we're seeking a Data Scientist to join our dynamic team in Madrid specialized in Machine Learning and Generative AI. As part of the AIF team you'll collaborate with a diverse group of experts to deliver best-in-class solutions for the agentic ecosystem with focus on evaluation, reusability and business impact. You'll leverage your technical expertise to propose, ideate and develop production-ready components that bring the state of the art in AI to BBVA.

Key job responsibilities

• Technical leadership: design and deliver reusable components in the agentic and evals spaces, as the lead of a small multidisciplinary team (data scientists and engineers).

• Insight Communication: Present findings and recommendations to stakeholders across the organization.

• Cross-Functional Collaboration:Work closely with product managers, engineers, and designers to implement data-driven solutions and integrate LLM capabilities effectively.

• Mentorship: Guide and mentor less experienced team members to foster growth and success.

• Best Practices Compliance: Ensure all deliverables meet Advanced Analytics governance standards and best practices.

Your Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related technical field.

• Experience: 2+ years of experience working as a data scientist in the LLM and agentic spaces, including working in multidisciplinary teams.

• LLM Expertise:advanced technical knowledge of LLMs, including model optimization, agent design, evaluation and embedding elicitation.

• AI Evaluation:methodological approach to the evaluation of performance and behavioural metrics, biases and risks of AI solutions, including hands-on experience with evaluation frameworks (like promptfoo, OpenAI Evals, etc).

• Communication Skills: Excellent ability to translate complex technical concepts into actionable business insights.

• Programming & ML Frameworks: expertise in Python, statistical modeling, and machine learning, with proficiency in the traditional data stack (spark, sklearn etc) and the agentic stack (langgraph, agents sdk, etc).

• 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 Science: Knowledge of ethical AI principles, data privacy laws (like GDPR, CCPA), and a commitment to responsible data science practices.

Nice to Have

• Previous experience in the financial industry.

• PhD in Computer Science, Statistics, Mathematics, or a related field.

• Team Lead: experience leading a technical team with focus on delivery and business impact.

• Cloud Computing: Experience in AWS, Google Cloud, or Azure for scalable data science solutions, including experience with Docker and Kubernetes.

• Open Source models: Experience developing solutions that leverage open source models (huggingface, etc).

• Experience in Causality: Knowledge and application of causal inference methods to identify and model cause-and-effect relationships.

Skills: