Data Scientist, Supply Chain (EMEIA)

Apple is where individual imaginations gather together, committing to the values that lead to
great work. It’s the diversity of our people and their thinking that inspires the innovation that
runs through everything we do. Here, you’ll do more than join something, you’ll add
something.

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.

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.

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.

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","internalDetails":null,"eeoContent":null