Google is looking to develop, evaluate, and improve its advertising products (Search, Display, Apps, TV, and Video) by applying scientific excellence and statistical methods to understand end-user behavior and the advertising ecosystem, especially in the context of privacy-preserving digital advertising.
Requirements
- coding (e.g., Python, R, SQL)
- querying databases or statistical analysis
- Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- Experience with machine learning on large datasets.
- Ability to select the right statistical tools to solve a data analysis problem.
- quantitatively trained with expertise in quantitative methodologies, and with an understanding of statistics and causal inference methods.
- comfortable working cross-functionally and thrive in a changing, science-driven organization, with an interest in data-driven advertising and marketing, and in the role of technology and science in shaping these areas.
Responsibilities
- Develop, evaluate and improve the entire range of Google's advertising products including Search, Display, Apps, TV and Video.
- Collaborate closely with engineers, analysts and product managers to develop new science and translate it into deployed products.
- Developing new ideas and methods that drive ad measurement and business generation, including paradigm-shifting ad-measurement science and products for the privacy-preserving future of digital advertising.
- 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. Develop and implement quantitative methods to answer those questions.
- 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 strategic questions using data.
Other
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 3 years of experience using analytics to solve product or business problems
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
- Ability to both teach others and learn new techniques such as differential privacy, with excellent leadership and self-initiation.
- Excellent written and verbal communication skills, with a passion for practical application of science to business.
