Senior Data Scientist - AI Evaluation & Product

Country: Spain

IT STARTS HERE

Santander (*****************) is evolving from a high-impact global brand to a technology-driven organization, and our people are at the heart of this journey. Together, we are driving a customer-centric transformation that values bold thinking, innovation, and the courage to challenge what's possible.

This is more than a strategic shift. It's an opportunity for motivated professionals to grow, learn, and make a real difference.

Our mission is to help people and businesses thrive. As part of the global AI Transformation team, you'll lead and manage strategic AI initiatives that drive impact across global companies and key functions. We are looking for someone who connects business, AI, and technology, ensuring that AI solutions create measurable value.

SENIOR / LEAD DATA SCIENTIST - AI EVALUATION & PRODUCT

THE DIFFERENCE YOU MAKE

Santander’s Data & AI Science (DAISci) team is the scientific engine of the bank’s AI transformation. We develop, evaluate, validate, and translate advanced Data Science and AI methods into robust products and capabilities that create measurable value across the organization.

DAISci operates at the intersection of scientific depth, product innovation, and real-world delivery. We combine rigorous analysis with pragmatic execution to address complex challenges and build scalable, reliable, and production-ready solutions.

Joining DAISci is an opportunity to work on strategic initiatives that shape how AI systems are assessed, understood, and continuously improved. You will help define evaluation methodologies, analytical capabilities, and products that enable teams to make better evidence-based decisions.

This role will be a key driver of AI evaluation and product excellence, combining technical and scientific depth with versatility, strong ownership, and the ability to move complex initiatives from ambiguity to delivery.

Main Responsibilities

We are seeking a Senior Data Scientist or Lead Data Scientist to drive the design and delivery of analytical frameworks, methodologies, and products focused on the evaluation and continuous improvement of AI systems.

The ideal candidate is a versatile and experienced Data Scientist who combines scientific rigor with a pragmatic product mindset. You will be comfortable going deep into technical problems, working across disciplines, and taking end-to-end responsibility for delivering meaningful outcomes in a dynamic environment.

• Lead Data Science initiatives end to end, from problem framing and prioritization through experimentation, implementation, deployment, and continuous improvement.
• Design methodologies, metrics, benchmarks, and testing strategies to evaluate the quality, performance, reliability, and behavior of AI systems.
• Apply statistical analysis, experimentation, machine learning, and advanced analytics to solve complex real-world problems.
• Translate business and user needs into clear analytical problems, technical requirements, and scalable solutions.
• Partner closely with Product, Engineering, Research, Validation, and Business teams to shape priorities, roadmaps, and delivery plans.
• Build high-quality analytical assets, prototypes, and production-oriented capabilities using sound software engineering and Data Science practices.
• Use data and evidence to generate actionable insights, support decisions, and identify opportunities for product and system improvement.
• Drive technical quality, reproducibility, testing, documentation, and maintainability across Data Science work.
• Navigate uncertainty, manage trade-offs, and proactively remove blockers to move initiatives forward.
• Mentor colleagues and provide technical guidance, contributing to a culture of scientific rigor, collaboration, and continuous learning.

What You'll Bring To The Table

• MSc or PhD in Computer Science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or another related quantitative field.
• Experience evaluating AI systems, machine learning models, or intelligent applications through benchmarking, experimentation, or quality-assessment frameworks.
• Knowledge of Generative AI, large language models, conversational systems, retrieval-augmented generation, or agent-based systems.
• Strong professional experience delivering Data Science or Machine Learning solutions in complex, real-world environments.
• Deep scientific...