Google’s advertising measurement team focuses on combining data at scale with formal science to make this possible. Our science helps make advertising useful and delightful to our users, and valuable and results-driven for our advertisers and publishers.
As a part of this team, data scientists bring scientific 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. 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.
In this role, individuals with an interest in understanding consumer behavior, advertising and privacy, a passion for business problems, and an interest in combining data and strategy will grow in the environment. You will be able to leverage their training and skills to leverage data and technology to make business decisions.Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers 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
- Direct the design and development of innovative measurement methodologies and products, setting the strategic technical direction for Conversion Lift and incrementality measurement.
- Architect and oversee the execution of complex experimental frameworks, including both user-level and geo-based randomized controlled trials, to rigorously establish causal impact at scale.
- Advance quantitative methods by integrating causal inference, statistical modeling, and machine learning techniques to solve highly ambiguous and complex measurement challenges.
- Drive the creation of scalable analysis pipelines, setting technical standards for the team and mentoring other data scientists on end-to-end analysis best practices.
- Serve a key technical lead and trusted cross-functional partner, and a mentor to junior data scientists.
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.
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
- Experience with causal inference methods such as split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators.
- Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- Applied experience with machine learning on datasets.