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.Data scientists in our team bring scientific excellence and statistical methods to bear on the challenges of advertising product creation, development and improvement with a deep, data-driven appreciation for the behaviors of the end user and the ecosystem. As a Data Scientist working on Ads Insights and Measurement, you will develop, evaluate and improve the entire range of Google's advertising products including Search, Display, Apps, TV and Video (YouTube). You will collaborate closely with a multi-disciplinary team of engineers, analysts and product managers to develop new science and to translate it into deployed products at scale. You will also play a key role in developing new ideas and methods that drive ad measurement and monetization, including paradigm-shifting ad-measurement science and products for the privacy-preserving future of digital advertising. In doing so, you will be a key part of building and driving impact on large-scale ad-systems both at Google and in the ad-tech and mar-tech industry as a whole, globally.You will have a broad set of technical skills, and are ready to take on some of modern advertising’s greatest challenges and make an impact on the entire global ads ecosystem. You will be quantitatively trained (advanced masters or PhD) with expertise in quantitative methodologies, and with a solid understanding of statistics and causal inference methods. You will have an interest in understanding consumer behavior, advertising and privacy with a passion for business problems. Additionally, you will use your training and skills to leverage data and technology to make business decisions. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA.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.