Data Scientist, AppleCare Digital at Apple (Austin, TX)
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The AppleCare Digital team is looking for an outstanding data scientist who is interested in crafting, developing and identifying data mining solutions that have direct and measurable impact on AppleCares support operations.
AppleCare has tremendous amount of data, and we have just begun exploration of the data in the areas of pattern detection, anomaly detection, predictive modeling, and optimization. The person in this position will work with various Online business managers to help identify viable analytics opportunities and then implement end-to-end analytical solutions. The role requires both a broad knowledge of existing data mining algorithms and creativity to invent and customize when necessary.
The job is located in Austin, Texas.
Work with multi-functional teams to define and refine business and research questions and use analytics, data mining, statistical techniques and machine learning to answer those questions. Conduct end-to-end analyses across all AppleCare touch points, including data gathering from large and sophisticated datasets, and analyses using advanced statistical and machine learning methods. Drive the generation of insights from raw, unstructured data to improve existing features and explore strategic directions.
Identify content, navigation and user experience elements that impact customer satisfaction, via deep analyses, A/B testing and multivariate testing approaches. Present findings from analytics and research and make recommendations to leadership and multi-functional business partners. Develop extensive knowledge of existing metrics. Build new metrics for performance measurement and advocate for changes to existing metrics where needed.
Lead data science & analytics projects through their entire lifecycle, including problem identification, scope management, analytics design, data gathering, data processing, analysis, algorithm design, deployment, measurement, and report generation. Partner with peers to build and prototype analyses pipelines that provide insights at scale. Evangelize adoption of best practices and build greater awareness of common data analysis pitfalls.
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