Senior Research Data Scientist, YouTube Ads

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.

About the job:

As a part of the YouTube optimization team, you will focus on delivering the best performances to the advertisers advertising on YouTube. In this role, you will work with advertisers who each have a business goal to achieve, such as getting the most conversions within their budget, the most clicks under a cost-per-click limit, building awareness of their brand, or many other more advanced settings. You will be a part of the team responsible for the backend systems that power all these optimizations.

In this role, you will manage advertisers’ spend and translate their business goals into real-time decision making on which ads to show, and to achieve this goal, you will develop large-scale optimization systems by utilizing a variety of different tools such as mathematical and statistical modeling, auction theory, risk management, and machine learning (deep networks, Bayesian, etc.).

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

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Partner with stakeholders across cross-projects and team settings to identify and clarify business or product questions to answer, providing feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
• Leverage custom data infrastructure or existing data models as appropriate, using specialized knowledge to design and evaluate models that mathematically express and solve defined problems with limited precedent.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python), independently formatting, re-structuring or validating data to ensure quality, and review the dataset to ensure it is ready for analysis.