Product Data Science & AI Agents Intern

Criteo
Paris, Ile-de-France, FrancePosted Sep 7, 2026
Data Science / ML

About the role

What You'll Do

Criteo is looking for new talented interns able to start from January 2027! The Product Analytics & Data Science team (PAX) uses cutting-edge technology, advanced statistics, machine learning and — increasingly — AI agents to tackle some of the most complex Product challenges at Criteo. We are evolving from a classic Data Science function toward an AI-native team: embedded alongside Product, R&D, GTM and Design, we frame the right problems early, prototype fast, and build trusted, reusable AI systems that help Criteo ship better products, faster, harnessing Product Intelligence. Think "diagnose the messy problem and build the tool that solves it": our approach combines product judgment, data, and hands-on technical fluency. In this internship, building AI agents and skills is at the center of the role. You will design, prototype and evaluate agents that change how PAX and our Product partners work. Agents you build and validate can graduate into owned, production capabilities for the team. Wondering how life is in the PAX team? Take a peek at Live my life with the Product Analytics & Data Science team. You will be assigned to one or several projects. The topics we tackle are wide and always evolving. Recent examples of the AI-native, agentic work you could contribute to: Build AI agents & skills for the team — design agents that automate recurring analytical work (e.g. a reporting agent, a Slack assistant, an AI skill), and ship them into our shared skills registry so the whole team benefits. Agent evaluation & guardrails — help build frameworks that evaluate multi-turn agents (LLM-driven personas talk to a real agent, LLM judges score relevance, coherence, helpfulness, safety and accuracy), so agentic products ship faster and more safely. Product intelligence prototypes — build retrieval / embedding-based systems for product understanding (e.g. semantic search over publisher content for supply discovery, brand safety, or audience creation). LLM-powe

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