Data Scientist, Supply Chain (EMEIA)

Confirmed live in the last 24 hours

Apple

San Francisco Bay Area

On-site

Posted March 31, 2026

Job Description

Summary

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 versatile Machine Learning Engineer / Next-Generation Data Scientist who can own the full lifecycle of intelligent solutions—from problem framing and data sourcing through to model development, deployment, and user-facing delivery. With the acceleration of AI-assisted coding, we are seeking someone who can operate as a full-stack problem solver, leveraging machine learning, advanced analytics, and modern development tooling to rapidly build scalable, production-ready solutions. You will work at the intersection of data, AI, and engineering—transforming complex operational and business challenges into impactful, deployable products. This role is ideal for someone who thrives in ambiguity, embraces new tools, and is motivated by delivering real-world outcomes rather than just models.

Minimum Qualifications

5+ years of experience in data science or machine learning, preferably within supply chain, operations, or a related domain. Proficiency in programming (e.g. Python) and experience with modern ML frameworks Experience using or willingness to adopt AI-assisted development tools (e.g. code generation, LLM-based tooling) Ability to work across the full technology stack (data pipelines, modelling, APIs, basic front-end) Understanding of software engineering principles (version control, testing, modular design) Familiarity with cloud platforms (e.g. AWS) and deployment patterns Strong problem-solving skills with the ability to operate in ambiguous environments Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders

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.

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