Data Scientist, Research, Ads Insight and Measurement

Key Skills

Data Analysis, Data science techniques, Effective communication skills, Leadership Skill, Python Programming, R Programming, SQL

Job Description

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
• Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.

Preferred qualifications:

• PhD degree in a quantitative discipline.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods with machine learning on large datasets.
• Ability to both teach others and learn new techniques such as differential privacy, with excellent leadership and self-initiation.
• Ability to select the right statistical tools to solve a data analysis problem.
• Excellent communication skills, with a passion for practical application of science to business.

About the job

As a Data scientists, you will apply scientific and statistical methods to the challenges of advertising product creation, development and improvement bringing a deep, data-driven appreciation for the behaviors of the end user and the ecosystem. Working within 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 closely with a multi-disciplinary team of engineers, analysts and product managers to develop new scientific approaches and translate them into deployed products at scale. You will play a key role in developing new ideas and methods that drive ad measurement and business generation, including paradigm-shifting ad-measurement science and products for the privacy-preserving future of digital advertising. In doing so, you will drive impact on ad systems both at Google and in the ad-tech and mar-tech industry. Armed with a broad set of technical skills, you will be 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 with expertise in quantitative methodologies, and a solid understanding of statistics and causal inference methods. You will understand consumer behavior, advertising and privacy, possessing a passion for business problems, and an interest in combining data and strategy.The US base salary range for this full-time position is $141,000-$202,000 + 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

• Support and shape new data-driven and privacy-preserving advertising and marketing products in collaboration with engineering, product and customer facing teams
• Collaborate with teams to define relevant questions about advertising effectiveness, incrementality assessment, the impact of privacy, user behavior, brand building, targeting, bidding etc.
• Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale
• Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution and answer strategic questions using data.
• Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and written and oral delivery of results to business partners and executives.