AIML - Sr. Data Scientist, Smart Home Devices
Apply NowCompany: Apple
Location: Seattle, WA 98115
Description:
Summary
Would you like to shape the future of Apple's Smart Home product experiences? Want to contribute to transforming how human beings interact with technology across hundreds of millions of customers and billions of interactions?
The Data Science team within Smart Home Devices is a part of the ML innovation organization with roots in computer vision, machine learning, and natural language processing. This role involves using a broad array of quantitative methods to find insights from user behavior, and designing approaches to evaluate unstructured data. This includes, but is not limited to; crafting creative techniques to analyze audio & video datasets, design metrics to understand user behavior & evaluate performance of machine learning models. We value team members who can quickly prototype & iterate on designs to high-quality implementations.
Description
As a Sr Data Scientist for Smart Home Devices, you will use statistical analysis of unstructured & structured data to deepen our understanding of how people use devices in the home environment including; iPhone, HomePod, Mac, and new products yet-to-be-released, and across use cases from music to home automation and general knowledge discovery.
You will be responsible to:
* Develop instrumentation & measurement framework that guide iterative product improvement for ML-based user experiences.
* Employ a diverse toolkit of analytical approaches, methodologies, frameworks, and technical strategies to uncover opportunities to improve user experience of ML based products. This includes insights on the performance of the underlying machine learning models that power feature experiences.
* Develop tools with scalable analytical and engineering approaches that drive a privacy-first strategy for analytical insight.
* Investigate product usage to identify weaknesses and opportunities to make the product better.
* Work cross-functionally with Engineering, Machine Learning and Product partners across the AIML ecosystem to ship ML-based features.
Would you like to shape the future of Apple's Smart Home product experiences? Want to contribute to transforming how human beings interact with technology across hundreds of millions of customers and billions of interactions?
The Data Science team within Smart Home Devices is a part of the ML innovation organization with roots in computer vision, machine learning, and natural language processing. This role involves using a broad array of quantitative methods to find insights from user behavior, and designing approaches to evaluate unstructured data. This includes, but is not limited to; crafting creative techniques to analyze audio & video datasets, design metrics to understand user behavior & evaluate performance of machine learning models. We value team members who can quickly prototype & iterate on designs to high-quality implementations.
Description
As a Sr Data Scientist for Smart Home Devices, you will use statistical analysis of unstructured & structured data to deepen our understanding of how people use devices in the home environment including; iPhone, HomePod, Mac, and new products yet-to-be-released, and across use cases from music to home automation and general knowledge discovery.
You will be responsible to:
* Develop instrumentation & measurement framework that guide iterative product improvement for ML-based user experiences.
* Employ a diverse toolkit of analytical approaches, methodologies, frameworks, and technical strategies to uncover opportunities to improve user experience of ML based products. This includes insights on the performance of the underlying machine learning models that power feature experiences.
* Develop tools with scalable analytical and engineering approaches that drive a privacy-first strategy for analytical insight.
* Investigate product usage to identify weaknesses and opportunities to make the product better.
* Work cross-functionally with Engineering, Machine Learning and Product partners across the AIML ecosystem to ship ML-based features.