Data-Scientist for Machine Learning
Pubblicato il 22-09-2026 - Mckinsey in Milano
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. 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.
Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won't find anywhere else.
Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey. You'll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. With colleagues across 65+ countries and over 100