QUALIFICATIONSBachelor's, Master's, or PhD in computer science, machine learning, applied statistics, mathematics, engineering, or a related fieldUp to 2 years of experience in programming or data science. (Note: Candidates with less experience but strong potential are encouraged to apply)Statistical Knowledge: Strong foundation in statistics and data analysis is required. Familiarity with GenAI core concepts is a nice-to-have (e.g., LLMs, RAG architecture, embeddings, and prompt engineering techniques)Proficiency in Python is a must. Experience with R, SQL, or Spark is highly valued. Familiarity with other tools such as, TensorFlow, PyTorch, or Hadoop is a plus. Familiarity with LLM tech-stack (e.g., Langchain, llamaindex) is also a plusProven ability to work effectively in team environments and with stakeholders at all levelsFamiliarity with AWS, GCP, or Azure is a nice-to-haveStrong verbal and written communication skills in Italian. Proficiency in English is required; knowledge of additional languages (e.g., Portuguese, German, French, Spanish)



is a plusWHO YOU'LL WORK WITHDriving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won't find anywhere else.When you join us, you will have:Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping yo

📌 Data Scientist (Milano)
🏢 Mckinsey
📍 Milano