Summary
At Apple, we believe in hard work, a fun environment, and the kind of creativity and innovation that only comes about when highly skilled people from diverse backgrounds approach problems from varying perspectives. We believe each customer interaction is an opportunity to delight, engage, and inspire - and that by focusing on the smallest of details, we can make big impacts with our customers!
The people here at Apple don't just build products - they craft the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.
Description
The Support Insight Data Scientist is responsible for deriving insights into the software quality of Apple's products. This is an exciting opportunity to directly impact our customers' experience and support Apple products throughout their life cycle.
The Core Functions of this role are as follows:
Craft and implement machine learning models, establish algorithms, and build tools to identify anomalies in metrics related to software field performance and product quality, while gaining a deep understanding of why customers contact Apple for support.
Use the output from those tools and models to help teams around Apple understand what product and software improvements are priorities to address.
Other Key Functions of this role include:
Collaborate with cross-functional teams to establish relationships, find opportunities, develop performance indicators, and communicate complex data analyses.
Build and refine anomaly detection tools and algorithms, improving our ability to identify and respond earlier to emerging signals in our data.
Research and implement new technologies and methods in data science, data engineering, and data visualization to elevate the technical proficiency and innovation of our team.
Construct and maintain dashboards and self-service tools, facilitating effective data analysis and insights sharing.
Handle large, complex data sets and use advanced analytics to tackle challenging problems.
