Machine Learning Scientist - AppleCare WW Demand Planning
Apple
San Francisco Bay Area, CA, US
Onsite
2026-07-06
Announced salary
$184,700 - $277,600
Low
$113K
Median
$149K
High
$196K
Market in San Francisco · BLS OEWS 2025
Estimated net pay
$10,389 - $14,966
/month · 32% withheld
after tax & contributions · Single, no dependents
Job description
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.
**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
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