Research Data Scientist, Ads Insights and Measurement

Google is one of the pioneers of modern digital advertising, and the enabler of a thriving 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.

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 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about 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, 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.
  

Minimum qualifications:

• 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.

Preferred qualifications:

• 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.