Knowledge Base Scientist Senior - Cdaio

Grupo Santander; Boadilla del Monte; Presencial; EUR 90.000 - 125.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Learning platforms and SantanderOpen A

Competitive salary with performance‑dr

BeHealthy wellness program

Descripción de la vacante

Grupo Santander is seeking a Senior AI Scientist to lead local Knowledge Base projects, translating global standards into scalable, reliable representations and graph-based data for AI agents. You will collaborate with data, engineering, and product teams to connect structured and unstructured data, shaping schemas and retrieval pipelines for conversational systems.

The role emphasizes translating architectural guidance into practical local solutions while aligning with global standards and

Formación

• 5–10 years of experience in AI, data science, or ML systems in production.
• Experience designing scalable data pipelines and AI systems.
• Experience collaborating with engineering, product, and business teams.

Responsabilidades

• Design, develop, and maintain local Knowledge Base instances with connected structured/unstructured data.
• Shape graph schemas, entity resolution, metadata, and retrieval pipelines for AI agents and conversational systems.
• Ensure alignment with global standards while adapting to local regulatory, linguistic, and business constraints.
• Collaborate with data engineering and product teams to translate architecture into practical local solutions.
• Contribute to evaluation frameworks, monitoring, and continuous improvement of Knowledge Base components.

Conocimientos

Python

Knowledge representation

Graph modeling

Semantic data structures

Information retrieval

Collaboration

Scientific rigor

Ownership and accountability

Herramientas

Graph technologies

Data processing tools

Descripción del empleo

As a Senior AI Scientist within Santander’s DAISci, you lead local AI knowledge base projects, translating global standards into scalable, reliable knowledge representations and graph-based data for AI agents. You’ll collaborate with data, engineering, and product teams to connect structured and unstructured data, shaping schemas and retrieval pipelines for conversational systems. Your work enables global AI initiatives to operate at scale while respecting local constraints. This role offers hands-on scientific leadership with impact across markets and platforms.

• learning platforms and SantanderOpen Academy
• competitive salary with performance-based bonuses
• BeHealthy wellness program
• Design, develop, and maintain local Knowledge Base instances with connected structured/unstructured data
• Shape graph schemas, entity resolution, metadata, and retrieval pipelines to support AI agents and conversational systems
• Ensure alignment with global standards while adapting to local regulatory, linguistic, and business constraints
• Collaborate with data engineering and product teams to translate architecture into practical local solutions
• Contribute to evaluation frameworks, monitoring, and continuous improvement of Knowledge Base components
• Share learnings with global Knowledge Base leadership and propagate reusable patterns across markets
• 5–10 years of experience in AI, data science, or ML systems in production
• Experience designing and operating scalable, reliable data pipelines or AI systems
• Experience collaborating across engineering, product, and business teams to deploy AI-enabled systems
• Ability to translate architectural guidance into practical local solutions
• Strong individual contributor with ownership in a distributed, multi-country AI organization
• Expertise in knowledge representation, graph modeling, semantic data structures, or information retrieval
• Practical background in ML and modern AI architectures related to structured knowledge and retrieval
• Strong Python programming skills and familiarity with data processing and graph technologies
• collaboration across cross-functional teams
• scientific rigor and methodical approach
• ownership and responsibility as an individual contributor
• knowledge representation
• graph modeling
• semantic data structures