Research Data Scientist, Ads Insights and Measurement

Google•Mountain View, CA

2d•$147,000 - $210,000

About The Position

Google is a pioneer in modern digital advertising, enabling a thriving and open internet advertising ecosystem. Ad-measurement is critical to this cycle, helping advertisers and platforms conceptualize, create, and plan successful campaigns, serve the right ads to the right users, allocate resources effectively, and audit ad efficacy. The advertising measurement team combines data at scale with formal science to make advertising useful and delightful for users, and valuable and results-driven for advertisers and publishers. As a Data Scientist on the Ads Insights and Measurement team, you will develop, evaluate, and improve Google's advertising products across Search, Display, Apps, TV, and Video (YouTube). You will collaborate with engineers, analysts, and product managers to develop new science and translate it into deployed products. You will also play a key role in developing new ideas and methods for ad measurement and monetization, including privacy-preserving ad-measurement science for the future of digital advertising.

Requirements

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, distributed computing.

Nice To Haves

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.

Responsibilities

Collaborate with teams to define relevant questions about advertising effectiveness, incrementality assessment, the impact of privacy, user behavior, brand building, targeting, bidding etc, then develop and implement quantitative methods to answer them.
Apply causal inference methods to design experiments, establish causality, assess attribution and answer strategic questions using data.
Analyze large, complex data sets by solving difficult, non-routine problems, using advanced analytical methods, conducting 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.
Partner cross-functionally to deliver business recommendations (e.g., cost-benefit analysis, experimental design, use of privacy preserving methods such as differential privacy), presenting findings effectively to stakeholders at multiple levels to drive decisions.