Machine Learning Data Scientist, Wireless Technologies & Ecosystems
Santa Clara Valley (Cupertino), California, United States
Software and Services
Wireless Technologies & Ecosystems (WTE) team is looking for an exceptional Data Scientist with a real passion for statistical analysis, building predictive models, and crafting end to end frameworks to capture analytical insights. You will work on building an end to end machine learning framework for unsupervised anomaly detection and root causing with the ultimate goal of improving the wireless experience for Apple customers. Be ready to make something extraordinary when you come here. Dynamic, reassuring people and innovative, industry-defining technologies are the norm at Apple. The people who work here have reinvented and defined entire industries with our products and services. The same real passion for innovation also applies to our business practices - strengthening our dedication to leave the world better than we found it. You should join Apple if you want to help deliver the next amazing Apple product.
- 4+ years industry experience in validating applied machine learning algorithms to understand real-world data (classification, supervised/unsupervised anomaly detection)
- Experience with analytics / big data analysis and data visualization
- Experience with Data Science algorithms including sensitivity analysis, decision trees, clustering, regression analysis and Information Theory
- Experience building anomaly/outlier detection frameworks and performing time series analysis
- Fluent in Java, Scala, Python
- Knowledge of database platforms like Hadoop, Vertica and fluency in SQL
- Experience in understanding of wireless systems & internet of things is a plus
- Excellent data storytelling and interpersonal skills to both technical and non-technical audiences
Building and improving Apple’s anomaly detection and root causing framework to identify pre and post launch software performance issues in a timely manner and find opportunities for optimizations. Improve unsupervised analysis techniques to develop a scalable anomaly detection framework that can be used across a large variety of technology domains with data sets of different sizes. Integration of machine learning algorithms to take data from both SQL and No-SQL databases. Developing various predictive models that can be applied to optimize Wireless Performance of Apple products. Working with various technology teams within the Product Development organization, onboarding different technology domains to the centralized anomaly detection framework and presenting analytical insights.
Education & Experience
PHD or M.S. in Computer/Electrical Engineering, Computer Science or equivalent