Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
  

Preferred qualifications:

• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
  
• Experience applying classical statistical inference techniques to large-scale data (e.g., variance reduction, Bayesian techniques) at or similar to Search's scale.
• Experience building statistical predictive modeling on large-scale data, at or similar to Search's scale.
• Experience partnering with engineering to build data products to scale best practices and improve operational efficiency.
• Experience presenting to senior leadership without a data science background.

About the job

Search has entered a new era. On the one hand, user's needs have gone beyond info-seeking to problem-solving. On the other hand, Search faces severe competition from 3p apps. Growth is a top line goal for Search in this new era.

The central Search growth data science team drives Search growth through data-driven growth strategies. We solve complex growth problems both at macro-level and micro-level, through growth measurement methodology development, growth data product development, and growth related analysis and insights.

This is a high profile role with executive level visibility. In this role, you will get to work on all aspects of data science, from data science methodology to tooling, both at macro strategy level and at daily product development level.

Responsibilities

• Search growth measurement methodology development (e.g., Empirical Bayes to conquer high uncertainty in live experiments, growth surrogates to connect short term live experiment signals with long-term growth).
  
• Launch based growth analysis and insights, understand different growth levers and their contributions to growth, and provide actionable insights on areas of investment.
  
• Work with 3p data to understand the competitive landscape, reason through its strategic implications, and make data-driven strategic action recommendations.
  
• Work with the Search growth engineering team to develop growth data products to scale growth best practices and increase the efficiency of growth operations.