Data Scientist, Brand Bidding Optimization, YouTube

Data Scientist, Brand Bidding Optimization, YouTube

YouTube advertisers range from global and local businesses trying to broaden their outreach among the internet audience, to those seeking immediate commercial engagement from interested users. The first group is referred to as Brand Advertisers, and they allocate their marketing budgets to a wide spectrum of channels, spanning both traditional (e.g., TV, print, billboards etc.) and digital media. The Brand ads team is responsible for designing optimal solutions that would help these advertisers achieve their marketing objectives on YouTube.

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The US base salary range for this full-time position is $166,000-$244,000. Our salary ranges are determined by role, level, and location. 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.

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Minimum Qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.

Preferred Qualifications:

• PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or a related technical field.

Responsibilities:

• Develop and implement optimization solutions across brand advertising on YouTube, using advanced techniques from machine learning, statistics, and operations research.
• Drive key project areas including ad relevance, creative recommendation, bidding optimization (e.g., maximizing advertiser ROI), and campaign impact measurement.
• Own the entire project lifecycle from solution design and engineering implementation to data-driven analysis and production launch.
• Identify and deliver enhancements to a business ecosystem in a fast-paced, high-growth environment.
• Collaborate seamlessly with software engineers and product managers to deliver solutions.