Google
Business Data Scientist, Economist
Google, Mountain View, California, us, 94039
Qualification
Minimum qualifications:Master's degree in Economics, Operations Research, Management Science, Statistics or related fields, or equivalent practical experience.3 years of experience in programming with R, Python, or SQL as demonstrated by projects, coursework or a public portfolio.Experience in causal inference, applied statistics, applied microeconomics or econometrics demonstrated by projects, coursework, or publications.Preferred qualifications:PhD in Economics, Operations Research, Management Science, Statistics, or in a related technical field.Experience working or prior internships in tech, public policy, data science, or economic consulting.Experience with modern causal inference, statistical tools and concepts demonstrated in past projects, coursework, or publications.Experience with synthetic controls and other panel data techniques, doubly robust estimators, and heterogeneous treatment effect estimators.Experience designing and analyzing randomized controlled trials or observational studies, demonstrated in past projects, coursework, or publications.Experience in causal inference, applied microeconomics, or econometrics demonstrated in projects, coursework, or publications.Description
In this role, you will be an internal consultant tasked with helping Google make better decisions through the use of causal inference. You will be working with many teams such as Finance, Product, Engineering, Business, Marketing, Legal, Policy, and Research. Your job will be to help decision-makers at Google make informed choices using econometric and statistical methods. You will have excellent communication skills in addition to technical expertise. You will translate vague questions into concrete problems on which you can design studies, and communicate findings to non-technical audiences while effectively conveying uncertainty and the assumptions used in your analysis.You will have experience with modern methods in causal inference and Machine Learning (ML). Google uses a large set of tools from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. When available methodologies are not well-suited for the problem you are trying to solve, you will need to develop new techniques and models.Responsibilities
Design and lead causal inference studies to answer critical business questions and evaluate effects of interventions.Present and communicate actionable insights and recommendations to executives, leaders, and cross-functional partners.Serve as a subject matter expert, reviewer and consultant for causal inference studies conducted by cross-functional teams.Develop and maintain internal tools and statistical software packages for investigative tasks and impact measurement.Summary
Master's degree in Economics, Operations Research, Management Science, Statistics or related fields, or equivalent practical experience.3 years of experience in programming with R, Python, or SQL as demonstrated by projects, coursework or a public portfolio.Experience in causal inference, applied statistics, applied microeconomics or econometrics demonstrated by projects, coursework, or publications.
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Minimum qualifications:Master's degree in Economics, Operations Research, Management Science, Statistics or related fields, or equivalent practical experience.3 years of experience in programming with R, Python, or SQL as demonstrated by projects, coursework or a public portfolio.Experience in causal inference, applied statistics, applied microeconomics or econometrics demonstrated by projects, coursework, or publications.Preferred qualifications:PhD in Economics, Operations Research, Management Science, Statistics, or in a related technical field.Experience working or prior internships in tech, public policy, data science, or economic consulting.Experience with modern causal inference, statistical tools and concepts demonstrated in past projects, coursework, or publications.Experience with synthetic controls and other panel data techniques, doubly robust estimators, and heterogeneous treatment effect estimators.Experience designing and analyzing randomized controlled trials or observational studies, demonstrated in past projects, coursework, or publications.Experience in causal inference, applied microeconomics, or econometrics demonstrated in projects, coursework, or publications.Description
In this role, you will be an internal consultant tasked with helping Google make better decisions through the use of causal inference. You will be working with many teams such as Finance, Product, Engineering, Business, Marketing, Legal, Policy, and Research. Your job will be to help decision-makers at Google make informed choices using econometric and statistical methods. You will have excellent communication skills in addition to technical expertise. You will translate vague questions into concrete problems on which you can design studies, and communicate findings to non-technical audiences while effectively conveying uncertainty and the assumptions used in your analysis.You will have experience with modern methods in causal inference and Machine Learning (ML). Google uses a large set of tools from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. When available methodologies are not well-suited for the problem you are trying to solve, you will need to develop new techniques and models.Responsibilities
Design and lead causal inference studies to answer critical business questions and evaluate effects of interventions.Present and communicate actionable insights and recommendations to executives, leaders, and cross-functional partners.Serve as a subject matter expert, reviewer and consultant for causal inference studies conducted by cross-functional teams.Develop and maintain internal tools and statistical software packages for investigative tasks and impact measurement.Summary
Master's degree in Economics, Operations Research, Management Science, Statistics or related fields, or equivalent practical experience.3 years of experience in programming with R, Python, or SQL as demonstrated by projects, coursework or a public portfolio.Experience in causal inference, applied statistics, applied microeconomics or econometrics demonstrated by projects, coursework, or publications.
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