At YouTube, we are seeking a Business and Marketing Data Scientist to join the Go-To-Market Impact Measurement team. You will partner with business leaders to shape YouTube's future by applying causal inference, Bayesian statistics, and machine learning to tackle business challenges and communicate actionable insights to decision-makers. The position requires a PhD in Economics, Statistics, Biostatistics, or a related field, and experience in causal inference, Bayesian methods, machine learning, and proficiency in R or Python and SQL.
At YouTube, we are seeking a Business and Marketing Data Scientist to join the Go-To-Market Impact Measurement team. You will partner with business leaders to shape YouTube's future by applying causal inference, Bayesian statistics, and machine learning to tackle business challenges and communicate actionable insights to decision-makers. The position requires a PhD in Economics, Statistics, Biostatistics, or a related field, and experience in causal inference, Bayesian methods, machine learning, and proficiency in R or Python and SQL.
Responsibilities include designing and executing causal studies to address critical business questions; leveraging advanced statistical models to extract insights from experimental and observational data; presenting actionable recommendations to executives and cross-functional partners; serving as a peer reviewer and consultant for causal studies; staying current with the latest advances in causal inference. You may collaborate with teammates and interns on research projects, and participate in conferences and internal events to keep our toolkit updated.
The role is based in Mountain View, California, United States, with a hybrid work arrangement as per policy. The base salary range for this full-time position is $177,550 to $198,000 plus a 15% bonus target, equity, and benefits determined by role, level, and location. Salary is determined by skills, experience, and education. Benefits details are provided by Google.
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Responsibilities include designing and executing causal studies to address critical business questions; leveraging advanced statistical models to extract insights from experimental and observational data; presenting actionable recommendations to executives and cross-functional partners; serving as a peer reviewer and consultant for causal studies; staying current with the latest advances in causal inference. You may collaborate with teammates and interns on research projects, and participate in conferences and internal events to keep our toolkit updated.
The role is based in Mountain View, California, United States, with a hybrid work arrangement as per policy. The base salary range for this full-time position is $177,550 to $198,000 plus a 15% bonus target, equity, and benefits determined by role, level, and location. Salary is determined by skills, experience, and education. Benefits details are provided by Google.
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