As a Data Scientist at Google, the business problem is to apply scientific and statistical methods to the issues of advertising product creation, development, and improvement, bringing a data-driven appreciation for the behaviors of the end user and the ecosystem. This involves developing, evaluating, and improving Google's advertising products and driving impact on ad systems both at Google and in the global ad-tech and mar-tech industries, especially in the context of privacy-preserving digital advertising.
Requirements
- Experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, and sampling methods.
- Experience with designing and building statistical forecast models as well as machine learning on datasets.
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
- Knowledge of structural econometric methods.
- Understanding of potential outcomes framework and familiarity with causal inference methods such as split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators.
- Work with large data sets. Solve difficult analysis problems, applying advanced analytical methods. Conduct end-to-end analyses that include data gathering and requirements specification, exploratory data analysis, model development, and written and oral delivery of results to business partners and executives.
Responsibilities
- Develop, evaluate, and improve the entire range of Google's advertising products, including Search, Display, Apps, TV, and Video (YouTube).
- Develop new scientific approaches and translate them into deployed products.
- Develop 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.
- Help suggest, support and shape new data-driven advertising and marketing products in collaboration with engineering, product and customer facing teams.
- 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.
- Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution and answer questions using data.
Other
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.
- Advanced degree in a quantitative discipline.
- Collaborate closely with a multidisciplinary team of engineers, analysts, and product managers.
- Possessing a passion for business problems and an interest in combining data and strategy.
