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Minimum qualifications:
- PhD degree in Economics, Statistics, Biostatistics or a related field, and experience in the job offered or in a Business and Marketing Data Scientist-related occupation.
- Position requires experience in the following: Causal inference; Bayesian statistics; Machine Learning; R or Python; and SQL.
About the job:
At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.
We are looking for a detail-oriented problem solver with broad knowledge of causal inference, Bayesian statistics, and machine learning. We use a large set of methodologies, ranging from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. The ideal candidate is comfortable wearing multiple hats and is passionate about conducting causal studies and helping stakeholders implement data-driven decisions. Creative problem-solving and stakeholder management skills are critical.
Given the nature of the position, someone who loves to learn new things will be successful. Effective data scientists on our team keep up with advances in the causal inference literature. When existing methodologies are not well suited for the problem at hand, you will be encouraged to develop new methods in collaboration with your teammates and work with our summer interns on research projects. We also attend relevant conferences and organize internal events to keep our toolkit updated and to educate the broader Google community.
The US base salary range for this full-time position is $177,550 - $198,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google.
Position reports to the Google Mountain View, CA office & may allow for a hybrid schedule as per Google policy.
Responsibilities:
- Design and execute causal studies to address critical business questions.
- Leverage advanced statistical models to find business insights in experimental and observational data.
- Present and communicate actionable insights and recommendations to executives and cross-functional partners.
- Serve as a peer reviewer and consultant for causal studies across the organization.
- Stay current with the latest advancements in causal inference.
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