Data Scientist - Supply Chain
The Product Launch Readiness (PLR) team plays a critical role in ensuring our EMEIA operations are prepared to support every new product we introduce. We’re looking for a Data Scientist with a passion for building intelligent, scalable solutions to join our team.
If you’re excited by the challenge of working with complex data, uncovering hidden patterns, and developing AI-powered tools that make a real impact—this role is for you.
In this role, you’ll focus on designing and deploying machine learning solutions that support operational readiness across Apple’s supply chain.
You will contribute to areas such as anomaly detection, intelligent document analysis, and the application of Generative AI to enhance decision-making and automation.
Partner closely with business teams, software engineers, and other data scientists to deliver robust, production-ready tools that help ensure our product launches are accurate, efficient, and well-orchestrated.
This is a hands-on, technical role that blends analytical depth with creative problem-solving.
Develop and deploy machine learning models for detecting anomalies and supporting operational decision-making.
Apply GenAI and natural language techniques to build intelligent solutions in support of product readiness.
Contribute to tools that extract insights from unstructured or semi-structured data using OCR and related methods.
Collaborate cross-functionally to translate business needs into actionable technical solutions.
Participate in the development and improvement of data pipelines and internal applications as needed.
- MSc/PhD in Computer Science, Data Science, Machine Learning, or a related field.
- Industry experience applying ML/AI techniques to solve practical problems.
- Proficiency in Python and common data science libraries (e.g. pandas, scikit-learn, PyTorch/TensorFlow).
- Experience with anomaly detection, GenAI/NLP techniques, or OCR technologies.
- Strong problem-solving skills and the ability to communicate technical ideas clearly to diverse stakeholders.
- Experience working in supply chain, logistics, or large-scale operational environments.
- Familiarity with master data concepts and data quality challenges.
- Exposure to software development practices and tools for building internal data applications.
- Passion for innovation, curiosity, and a willingness to work in a fast-paced, dynamic environment.