About the job

Youtube optimization team focuses on delivering excellent performances to the advertisers advertising on Youtube. Each advertiser has a business goal to achieve. That goal could be getting most conversions given their budget, most clicks given a limit on cost-per-click, create awareness about their brand, or many other more advanced settings. Our team is responsible for the backend systems that power all these enhancements.

We manage advertisers’ spend and translate their business goals into real-time decision making on which ads to show. To achieve this goal we develop 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).

The US base salary range for this full-time position is $150,000-$223,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into metrics, or mathematical models.
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to 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 tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.