Research Data Scientist, Ads Insights

Socket.dev; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Google in Mountain View seeks a Data Scientist for Ads Insights to develop data-driven methods and privacy-preserving advertising solutions. You will drive experiments, causal inference, and scalable analytics across complex ad systems.

The role involves collaborating with cross-functional teams to define questions on advertising effectiveness, incrementality, and user behavior, and to implement quantitative approaches that influence product decisions and business outcomes.

Qualifications

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

Responsibilities

• Help suggest, guide, 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., and develop and implement quantitative methods to answer those questions.
• 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 questions using data.
• Build and prototype analysis pipelines iteratively to provide insights at scale, and develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed for product development.

Skills

Analytics experience

Python

R

SQL

Data-driven decision making

Education

Master's degree in quantitative field

PhD degree

Tools

Python

R

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 work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

Preferred qualifications:

• 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 a PhD degree.

About the job:

Google’s advertising measurement team focuses on combining data at scale with formal science to make this possible. Our science helps make advertising useful and delightful to our users, and valuable and results-driven for our advertisers and publishers.

As a Data Scientist working on Ads Insights, you will play a key role in developing new ideas and methods that drive business generation, including paradigm-shifting 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Help suggest, guide, 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., and develop and implement quantitative methods to answer those questions.
• 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 questions using data.
• Build and prototype analysis pipelines iteratively to provide insights at scale, and develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed for product development.