Google is one of the pioneers of modern digital advertising, and the enabler of a growing and open internet advertising ecosystem that supports millions of users, publishers and advertisers. Modern digital advertising is a scaled business that relies on a mutually-reinforcing cycle of ad-sales, ad-planning, ad-implementation/optimization and ad-measurement. Ad-measurement is key to this cycle, and consequently is critical to the business. Among other things, good measurement helps advertisers and platforms to conceptualize, create and plan successful campaigns; to serve the right ads to the right users; to help allocate resources to the right channels and to allocate bids in auctions and budgets across campaigns appropriately; and to audit the efficacy of ads in delivering on various campaign goals. Good measurement is also key for ensuring users are exposed to ads they find useful; and for ensuring publishers, content creators and other ad-tech firms can monetize their content appropriately; both of which are key to sustaining an open and vibrant internet.
Data scientists in our team bring scientific and statistical methods to bear on the issues 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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
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benefits at Google.
Responsibilities
- 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 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 questions using data.
- Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed.
- Conduct end-to-end analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and written and oral delivery of results to business partners and executives.
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 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 3 years of work experience with a PhD degree.
Preferred qualifications:
- 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
- Applied experience with machine learning on large datasets.