Research Data Scientist, Ads Metrics, Core Metrics
Google · New York, New York, United States · $147K–$210K/yr
Software Development · 10,001+ employees
About the role
Collaborate with stakeholders to translate business questions into tractable analysis and mathematical models. Own the end-to-end process of gathering, extracting, and validating data to inform product launch decisions.
What they look for
Requirements
Requires a Master's degree in a quantitative field and at least 3 years of relevant work experience. Candidates must be proficient in coding languages like Python, R, and SQL, and possess strong analytical skills.
Benefits
Full 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:
Ads Metrics is a data science team which supports the Search Ads and Ads on Google Experiences (SAGE) organization in developing Google's most important ad products. Core Metrics is a subteam in Ads Metrics that focuses on developing and improving SAGE's Northstar metrics (including long-term business and ads blindness) which are used organization-wide to inform launch decisions. We manage challenging problems in measurement methodology and experiment design, while also staying focused on helping the organization make better business decisions from data.
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:
- 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.
