Data Scientist

Artefact is a new generation of data service providers specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.

The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefacts offers (and more): data marketing, data governance, strategy consulting, product owner…

• Hands-on experience building personal, academic, or internship projects using Python or JavaScript/TypeScript.
• A genuine interest in Artificial Intelligence, especially modern LLMs (OpenAI, Claude, open-source models).
• Prior hands-on experience, even in small personal sandbox projects, with frameworks like LangGraph or LangChain, alongside core concepts like agents, tool calling, and RAG.
• Ability to consume REST APIs and build simple end-to-end backend and/or frontend workflows.
• Technical reading comprehension in English (most AI documentation, research, and libraries are in English).
• Basic familiarity with vector databases or search backends (e.g., Pinecone, Supabase / pgvector, Chroma, Weaviate).
• Comfortable using Git for version control, branching, and pull requests.

• Frontend & App Frameworks: Exposure to Next.js, React, or modern UI libraries (e.g., Tailwind CSS, shadcn/ui) to quickly turn AI flows into functional interfaces.
• Structured Data & Schemas: Experience enforcing structured outputs from LLMs using schemas (e.g., Pydantic in Python, Zod in TypeScript).
• LLM Observability & Debugging: Familiarity with tracing or evaluation tools like LangSmith, Langfuse, or Helicone.
• Deployment Basics: Experience deploying full-stack apps to platforms like Vercel, Railway, or Render, AWS, Azure, GCP.
• Active Experimentation: A visible GitHub repo, live demo, or hobby project that shows curiosity about the latest AI releases and tooling.