Research Data Scientist, Ads Insights and Measurement

• Help suggest, support and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product and customer facing teams.
  
• Collaborate with teams to define relevant questions about advertising effectiveness, incrementality assessment, the impact of privacy, user behavior, brand building, targeting, bidding etc., and develop and implement quantitative methods to answer those questions.
  
• Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale.
  
• Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution and answer strategic questions using data.
  
• Work with large, complex data sets. Solve difficult problems, applying advanced analytical methods. Conduct analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and written and verbal delivery of results to business partners.
  

Minimum qualifications:

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

Preferred qualifications:

• 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 a PhD degree.
  

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