Google

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
• Experience working on applied data science problems in Ads (e.g. targeting, bidding, ranking, etc.)
• Experience with Deep Learning.

About the Job

Data scientists in our team bring scientific excellence and statistical methods to bear on the challenges of advertising product creation, development and improvement with a deep, data-driven appreciation for the behaviors of the end user and the ecosystem. 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 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 generate business, including paradigm-shifting Ad-measurement science and products for the privacy-preserving future of digital advertising. In doing so, you will be a key part of building and driving impact on large-scale ad-systems both at Google and in the Ad-tech and mar-tech industry as a whole, globally.

You will have a broad set of technical skills who are ready to take on some of modern advertising’s greatest challenges and make an impact on the entire global ads ecosystem. You will be quantitatively trained (advanced Master's or PhD) with expertise in quantitative methodologies, and with an understanding of statistics and causal inference methods. You will be able to leverage your training and your skills to leverage data and technology to make business decisions. They will be comfortable working cross-functionally and thrive in a fluid, science-driven organization.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. 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.

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 tractable analysis, evaluation 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 relevant tools (e.g., SQL, R, Python). Independently format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.