Senior Data Scientist / AI Engineer

About the team

Our Data Science team builds products that help Penguin Random House make smarter, ethical and commercially responsible decisions. We combine data science, machine learning and the latest developments in AI to solve real business problems and turn promising ideas into tools people can actually use.

Based in Barcelona, you’ll be part of a global Data Science & AI community, collaborating closely with colleagues in the US and across Penguin Random House to share technology, ideas and best practices.

About the role

As a Senior Data Scientist – Applied AI, you’ll take complex problems from exploration and experimentation through to production. You’ll work across machine learning, NLP and generative AI, selecting and evaluating the right approaches to build effective, reliable solutions.

You’ll bring strong Python and data science skills alongside hands-on experience with modern AI, including LLMs, retrieval and agents. You’ll also work closely with ML and Infrastructure Engineers to ensure the solutions you build are reliable, scalable and maintainable in production.

As a senior member of the team, you’ll be expected to make sound technical decisions, challenge assumptions, support other Data Scientists and help shape the approaches and technologies we adopt.

We also care deeply about responsible AI. You’ll consider privacy, security, provenance, model limitations and appropriate human oversight as part of how solutions are designed and built.

What you’ll be doing

• Lead data science and applied AI work from exploration and experimentation through to production

• Develop and evaluate machine learning, NLP and generative AI solutions, selecting the approach that best fits the problem

• Build applications using LLMs and techniques such as structured generation, embeddings, semantic search, retrieval, RAG, tool use and agents

• Develop production-quality Python services, APIs and data pipelines that integrate models and AI capabilities into real products and workflows

• Design experiments and evaluation frameworks to compare approaches, measure quality and understand where systems succeed and fail

• Work with structured and unstructured data at scale using SQL and modern data platforms

• Collaborate with ML and Infrastructure Engineers on deployment, containerization, monitoring and production operations

• Work directly with colleagues across the business to turn open-ended questions into practical technical solutions

• Support other Data Scientists and contribute to shared technical standards, reusable components and best practices