SummaryApple is where individual imaginations gather together, committing to the values that lead togreat work. It's the diversity of our people and their thinking that inspires the innovation thatruns through everything we do. Here, you'll do more than join something, you'll addsomething.DescriptionWe are looking for a Data Scientist to join our EMEIA Supply Chain team who can bridgetraditional machine learning with modern Generative AI. You will apply advanced analytics,statistical modelling, and LLM-driven solutions to high-impact supply chain challenges atscale. If you're equally comfortable building a classical forecasting model as you arearchitecting an LLM-powered application, we'd love to hear from you.ResponsibilitiesDevelop and deploy machine learning models (supervised, unsupervised, and deeplearning) for supply chain use cases including demand forecasting, inventoryoptimisation, and logistics planning.Design and build Generative AI applications leveraging Large Language Models,including RAG pipelines, prompt engineering, and fine-tuning for domain-specificsupply chain problems.Architect end-to-end data science solutions from concept through productiondeployment, blending open-source technologies with Apple's proprietaryinfrastructure.Evaluate business needs through close collaboration with EMEIA supply chainstakeholders, and present findings and recommendations to leadership in clear, non-technical terms.Minimum Qualifications5+ years of experience in data science or machine learning, preferably within supplychain, operations, or a related domain.Strong proficiency in Python for data science, including libraries such as pandas,scikit-learn, TensorFlow, or PyTorch.Solid understanding of both supervised and unsupervised machine learningtechniques including regression, classification, clustering, time-series forecasting,and deep learning.Experience with SQL and modern data platforms for querying, transforming, andmanaging large-scale datasetsPreferred QualificationsHands-on experience with Large Language Models including prompt engineering,fine-tuning, and building LLM-powered applications using RAG architectures, vectordatabases, and embedding models.Familiarity with agentic AI patterns and orchestration frameworks (e.g., LangChain,LlamaIndex, CrewAI, or similar).Experience developing APIs or service layers (e.g., FastAPI, Flask) and exposure tofront-end frameworks (e.g., React, Streamlit) for building interactive dataapplications.Knowledge of MLOps practices including model versioning, monitoring, and CI/CDfor ML pipelines.Education & ExperienceBSc or equivalent in Computer Science, Mathematics, Statistics, OperationsResearch, Engineering, Physics, or a related quantitative field.MSc or PhD in a quantitative discipline is preferred but not required; equivalentprofessional experience will be considered.Foundational understanding of Generative AI concepts is expected; formal training orcertifications in AI/ML are a plus.