Machine Learning Scientist - AppleCare WW Demand Planning

Imagine what you could do here! The people here at Apple don’t just create products — they build the kind of wonder that’s revolutionized industries. It’s the diversity of those people and their ideas that encourages the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. At Apple, inclusion is a shared responsibility, and we work together to develop a culture where everyone belongs and is inspired to do their best work.

At Apple, the customer experience doesn't end with a purchase; that is just the beginning. The Worldwide Demand Planning team ensures service continuity across every channel. We forecast the inventory needed to support customers everywhere—whether they visit an Apple Store, mail in a device, or seek help through our external Carrier and Insurance partners. Our scope spans across whole unit devices, repairable parts such as battery or display, packaging, and tools required for repair. We ensure the right forecast for parts is in place at our warehouses, Retail stores, and thousands of Service Providers worldwide.

Description

We are expanding our technical capabilities and seeking a Machine Learning Scientist to help us operationalize and scale our forecasting models. You will join a diverse team of data scientists and planners, bringing the specific engineering rigor needed to turn complex analyses into robust, self-improving production systems. Your work will enable the team to move faster and deploy models that directly impact resource availability for millions of customers. Join us to build the infrastructure that powers the heartbeat of AppleCare.

Minimum Qualifications

Master’s or PhD in Computer Science, Machine Learning, Statistics, Industrial Engineering, Operations Research, or a related quantitative field with 3+ years of industry experience in deploying Machine Learning models OR Bachelor’s degree in a quantitative field with 3+ years of industry experience in deploying Machine Learning models.

Applied Machine Learning: Practical experience creating and deploying models in real-world environments, with specific expertise in Time Series forecasting, Anomaly Detection, or Optimization. Software Engineering Proficiency: Expert proficiency in Python, with a strong grasp of software design principles (Object-Oriented Design), data structures, and writing testable, maintainable code beyond just scripting. Data Systems: Expert-level SQL skills and experience working with large-scale distributed data processing frameworks (e.g., modern cloud data warehouses like Snowflake, Oracle, Spark, Hadoop, etc.). Communication: Superior ability to translate meticulous mathematical concepts into clear, actionable insights for non-technical stakeholders and leadership.

Preferred Qualifications

Production Engineering: Proven experience taking models from research prototypes to production systems (using CI/CD, APIs, and containerization). Creative Modeling: Ability to engineer novel features and apply advanced Time Series or ML techniques to solve complex demand challenges. Business Insight: Proficiency in translating raw data into compelling narratives that drive strategic business decisions. Mentorship: Proven track record of up-skilling teammates, bridging the gap between statistical analysis and software engineering.