Senior Data Scientist (Multiple Positions)Location: SeattleEmployment Type: RegularJob Code: A AResponsibilitiesProvide insights to influence product development using advanced statistics, machine learning (ML), and programming. Apply quantitative analysis to understand data, provide actionable insights, and measure the data through experimentation and causal inference. Partner with Product, Engineering, User Research, Designer and other teams to solve problems and identify trends and opportunities.
Build classification and clustering machine learning models to understand data characteristics. Visualize and synthesize analytics and statistical approaches into easy-to consume storylines for the team to provide indicated actions for executive audiences. Work with large, complex data sets to solve difficult, non-routine analysis problems by applying advanced analytical methods as needed.
Apply quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and millions of businesses. Interact cross-functionally, making business recommendations with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information. Mentor junior and intern Data Scientists.QualificationsMust have a Master's degree or foreign equivalent degree in Computer Science, Engineering (any), Data Science, Analytics (any), Operations Research, Mathematics, or a related quantitative field, and 2 years of related work experience; OR a Bachelor's degree or foreign equivalent degree in Computer Science, Engineering (any), Data Science, Analytics (any), Operations Research, Mathematics, or a related quantitative field, and 5 years of post-bachelor's, progressive related work experience.
Of the required experience, must have 2 years of experience in each of the following: Mining, preparing, cleansing, and analyzing structured data using statistical computing languages SQL, Python, and R for metrics creation, root cause analysis, and model preprocessing; Using SQL queries and data visualization tools to gather, process, visualize, and analyze large quantities of data to detect trends and anomalies in support of business opportunity identification; Analyzing large quantities of data to detect anomalies using Python and statistical analysis techniques; Developing graphical and visual representations of data using business intelligence tool Excel or Tableau; and Building, tuning, and validating statistical models for regression and classification problems in Python and R.