Senior Data Scientist, Proactive at Apple (Cupertino, CA)

Play a part in the next revolution in human-computer interaction. Do you get excited by driving product impact via measurement and evaluation, for products and services used by hundreds of millions of people globally? The vision for the Proactive Intelligence organization is to improve Apple platforms by better understanding, anticipating, and adapting to user behavior by using machine learning to build phenomenal predictive features that are built right into Apple platforms.
You will partner closely with the Proactive Intelligence leadership team, as well as with product and engineering teams, to devise the data insights and reporting best practices that guide the development of current and new intelligent experiences across iPhone, iPad, HomePod, Mac, Watch, tv, and across dozens of locales.

The Proactive Intelligence organization is looking for an experienced and highly motivated data scientist! You will partner across engineering groups building on-device intelligence, and evangelize a culture of using data to measure, understand, and improve our products and features. You have a background that fuses data science, engineering, and product thinking. You have years of practical experience building measurement, evaluation, and insights to improve products.


Research and develop evaluation methods to improve the quality of Proactives user-facing products.

Drive the generation of insights from raw, unstructured data to improve existing features and explore future directions.

Develop extensive knowledge of existing metrics, advocating for changes where needed.

Work closely with engineering teams to guarantee the consistency and validity of metrics across Proactive.

Conduct analysis that includes data gathering and requirement specifications, processing, analysis, and report generation.

Drive weekly metrics reports, and quarterly metrics presentations, with Apples leadership teams.

Tackle difficult, non-routine analysis problems, applying advanced analytical methods as needed.

Partner with your peers to build and prototype analysis pipelines that provide insights at scale.

Evangelize adoption of best practices and build greater awareness of common data analysis pitfalls.

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