eBay: Data Scientist – Statistician

Conduct A/B tests and deep dive statistical analysis/modeling for marketing analytic at eBay. Critical skills/experience on R, SQL, and online A/B experiments required.

eBay At: eBay
Location: Bellevue, WA
Web: www.ebay.com

Email to Jed Fransen, jfransen@ebay.com.

Primary Job Responsibilities
This position is in the Internet Marketing analytic team at eBay, and responsible for analyzing the search engine performance in driving quality traffic to eBay marketplace. The candidate should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of high-impact business questions and present the insights in concise and effective manner. Technical aptitude, familiarity with common data mining and statistical packages are required. The candidate should also be an effective communicator capable of independently driving issues to resolution and communicating insights to both technical and non-technical audiences.

Job Requirements
  • Assist senior analysts and work with Business Units, Product Development, and Product Management teams to identify and answer important business and product questions. Communicate insights to executives of eBay North America and Europe Internet Marketing business.
  • Work with global teams on ad-hoc projects and take a key analytic role in international projects and initiatives.
  • Research, evaluate, implement, and present statistical methods to provide actionable insights.
  • Define new metrics and help drive business/engineering team using the right metrics.
  • Research new ways for modeling and predicting user behavior.

Basic Qualifications
  • M.S. or Ph.D. in applied mathematics, statistics, machine learning, engineering, economics, or related fields.
  • Experience solving analytical problems using quantitative approaches
  • Strong SQL and relational database knowledge
  • Strong knowledge using major statistical analysis tools such as R, Matlab, or SAS
  • critical skills/experience in R, SQL, A/B tests
  • Creation and implementation of statistical predictive models including such algorithms as neural networks, decision tress, regression, clustering, association etc
  • Online experimentation and associated statistical models e.g. A/B testing and test/control groups
  • Knowledge assessing the statistical significance of performance differences between models is preferred; e.g. hypothesis testing to compare a model with new features to an already deployed model.
  • Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner with both technical and non-technical customers