Data Scientist, Supply Chain (EMEIA)

Data Scientist, Supply Chain (EMEIA)

Apple

Cork, Ireland

Description

We are looking for a Data Scientist to join our EMEIA Supply Chain team who can bridge traditional machine learning with modern Generative AI. You will apply advanced analytics, statistical modelling, and LLM-driven solutions to high-impact supply chain challenges at scale. If you’re equally comfortable building a classical forecasting model as you are architecting an LLM-powered application, we’d love to hear from you.

Responsibilities

• Develop and deploy machine learning models (supervised, unsupervised, and deep learning) for supply chain use cases including demand forecasting, inventory optimisation, and logistics planning.
• Design and build Generative AI applications leveraging Large Language Models, including RAG pipelines, prompt engineering, and fine-tuning for domain-specific supply chain problems.
• Architect end-to-end data science solutions from concept through production deployment, blending open-source technologies with Apple’s proprietary infrastructure.
• Evaluate business needs through close collaboration with EMEIA supply chain stakeholders, and present findings and recommendations to leadership in clear, non- technical terms.

Minimum Qualifications

• 5+ years of experience in data science or machine learning, preferably within supply chain, 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 learning techniques including regression, classification, clustering, time-series forecasting, and deep learning.
• Experience with SQL and modern data platforms for querying, transforming, and managing large-scale datasets.

Preferred Qualifications

• Hands-on experience with Large Language Models including prompt engineering, fine-tuning, and building LLM-powered applications using RAG architectures, vector databases, 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 to front-end frameworks (e.g., React, Streamlit) for building interactive data applications.
• Knowledge of MLOps practices including model versioning, monitoring, and CI/CD for ML pipelines.

Education & Experience

• BSc or equivalent in Computer Science, Mathematics, Statistics, Operations Research, Engineering, Physics, or a related quantitative field.
• MSc or PhD in a quantitative discipline is preferred but not required; equivalent professional experience will be considered.
• Foundational understanding of Generative AI concepts is expected; formal training or certifications in AI/ML are a plus.