Data Scientist, AppleCare Digital
Austin , Texas , United States
Machine Learning and AI
Posted: Mar 23, 2021
Weekly Hours: 40
Role Number: 200230803
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don't just craft products - they build the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple and help us leave the world better than we found it! 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 AppleCare's 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.
- Confirmed experience in a Data Science role in the areas of Natural Language Processing, Analytics, Machine Learning, Statistics or related field. Experience crafting, conducting, analyzing, and interpreting experiments and investigations.
- Experience articulating and translating business questions and using analytics and data science techniques to answer those questions.
- Strong working knowledge of data science algorithms including tree based algorithms, probability networks, association rules, clustering, regression, and neural networks.
- Experience working in diverse multi-functional teams to drive results, providing expertise and direction on analytics, data science, experimental design, and measurement.
- Expertise in statistical data analysis such as linear models, multivariate analysis, sampling methods, and causal inference.
- Experience with common data science toolkits, such as R/Python and various data processing and machine learning libraries.
- Proficiency in using query languages such as SQL and Hive.
- Experience in producing powerful visualizations and dashboards that balance both art and science (using Tableau/D3, etc).
- Strong interpersonal skills and the ability to naturally explain difficult technical topics to everyone from data scientists to engineers to business partners and leaders.
- Ability and comfort working independently and making key decisions on projects.
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.
Education & Experience
Advanced degree (MS or PhD preferred) in a quantitative field such as Data Science, Machine Learning, Statistics, Operations Research, Engineering, Computer Science, Mathematics, Physics, Bioinformatics, Economics, Psychology, or a similar quantitative field.