Job Details
Posted date: Sep 05, 2024
There have been 2 jobs posted with the title of Staff Data Scientist, Product, Ads Privacy and Safety all time at Google.Location: Kirkland, WA
Level: Director
Estimated salary: $210,000
Range: $168,000 - $252,000
Description
Conduct in-depth analyses of fraud vectors, identifying trends, and patterns to inform mitigation strategies.Utilize advanced statistical techniques to proactively identify emerging threats and risks to the advertising platform.
Develop success metrics and quantitative frameworks for Ads Safety and Risk and guide strategic decisions based on data insights.
Generate actionable insights to guide cross-functional teams in developing safer and more effective advertising products.
Measure the effectiveness of Ads Safety efforts, quantifying the impact on user trust, business, and risk reduction.
Ads Privacy and Safety Analytics and Insights focuses on data-driven objectivity, accountability, vigilance, and user-centricity to ensure the integrity of our advertising platform. In this role, you will develop unbiased frameworks to enable continuous measurement of the health of the business and deliver impact assessments across risk, business and user trust efforts and help proactively counter threats and challenges by enabling precise measurement “up funnel” and ensuring that we are addressing the right problems.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
The US base salary range for this full-time position is $168,000-$252,000 + 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.
Qualifications
Minimum qualifications: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, related quantitative field, or equivalent practical experience.7 years of experience years in data science or machine learning.
Experience in scripting (SQL, Python, or R) or statistical analysis (e.g., R, Stata, SPSS, SAS) in a matrix organization. Experience with Machine Learning (ML) algorithms and statistical modeling.
Preferred qualifications: Master's degree or PhD in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
Experience in fraud detection, risk assessment, or business health monitoring.
Experience presenting complex data findings to non-technical stakeholders and influence decision-making at senior levels.
Experience leading and influencing cross-functional teams without direct authority.
Ability to mentor junior team members and drive a culture of data-driven decision-making.
Extended Qualifications
Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, related quantitative field, or equivalent practical experience.7 years of experience years in data science or machine learning.
Experience in scripting (SQL, Python, or R) or statistical analysis (e.g., R, Stata, SPSS, SAS) in a matrix organization. Experience with Machine Learning (ML) algorithms and statistical modeling.
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