Machine Learning Engineer (App Store), Apple Media Products Data Science at Apple (Culver City, CA)
At Apple, phenomenal ideas have a way of becoming great products, services, and customer experiences very quickly. If you are an ambitious, high-energy individual who is not afraid of challenges, were looking for you.
Apple is seeking an expert Machine Learning Engineer or Applied Scientist to join a team passionate about building ML models for user acquisition, advertisement, and media applications, covering the App Store.
This role will involve working with Internet-scale data from the App Store advertisement ecosystem and building/implementing products to drive business and marketing strategy.
The teams culture is centered around rapid iteration with open feedback and debate along the way. We encourage independent decision-making and taking calculated risks. AMP Data Science collaborates with partners across product, marketing, design, engineering, and business teams.
Our mission is to drive innovation at Apple through deep quantitative research.
Collaborate with App Store stakeholders and partners to design Machine Learning models that help us better understand, acquire, and advertise to our App Store users.
Engineer end-to-end Machine Learning products which provide App Store partners with a granular understanding of customer preferences and user value drivers, including scalable solutions for customer acquisition and targeting.
Support engineering, business, and marketing teams at Apple in optimizing all customer touch points by detecting usage patterns with data-driven / machine-learned methods and translating them into impactful solutions.
Dive deep into large-scale data sources to uncover opportunities for Machine Learning automation, predictive methods, and quantitative modeling across the App Store.
Partner with other Apple organizations on data engineering, data governance, evangelizing Machine Learning, and democratizing data.
Increase internal adoption of ML and AI products.
Your creative solving skills will be utilized daily.
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