苹果Intern, Greater China Data Scientist, Ad Platforms

任职要求

Minimum Qualifications
• Fluent and project experience in Python and SQL
• Ability to work and distill information from large data sets and results, with attention to details and critical thinking mindset, self driven and able to come up with hypothesis and corresponding deep dives
• Knowledge and experience in advanced analytical techniques and statistical methods, including regression, classification, clustering, etc.

Preferred Qualifications
• BS/MS student in a quantitative field, such as Business Analytics, Applied Mathematics, Engineering, Operations Research, Social Sciences, Statistics, or equivalent professional experience.
• Comfortable with other advanced analytics tools such as Spark, and Tableau.
• A strong passion for business intelligence and analytics, work closely with the business teams, understand various requirements, and manage complex projects related to data-driven initiatives.
• Understanding of digital advertising landscape and KPIs for web / mobile / apps. Search engine marketing expertise, app-install marketing, and channel partners (e.g., agencies, mobile measurement) preferred.

工作职责

A successful candidate will have deep experience in applied methods (statistical analysis, predictive modeling, classification, impact measurement), experience building reporting dashboards, and using those methods and tools to drive key business insights. These insights include but not limited to market growth opportunities and strategies, product opportunities, and advertiser behaviors. The role requires data science skills (i.e., a broad knowledge of existing data mining algorithms), strong business acumen, and knowledge on mobile ads market. You’ll work closely with a broad range of data professionals as well as business leaders. You’ll be working on projects where tools and methods are used to drive real-world business value.   - Support DI, Product, Marketing, Partner Development with analytics for customer insights and product opportunity areas.  - Quantify the impact of product, sales, and marketing initiatives on customer happiness, and future behavior.  - Manage business analytic projects through all phases, including data quality, data modeling, algorithm/KPI development, statistical analysis, data visualization, and presentation of results and deliverables.  - Monitor usage metrics, understanding business-based explanations for large scale trends, and patterns in customer lifecycle behavior.