Data Scientist, Product, Support Platform Data Science
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Minimum qualifications:
- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 3 years of experience with statistical data analysis, data mining, and querying (e.g., SQL).
- 1 year of experience managing analytical projects.
Preferred qualifications:
- 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL).
- Experience in experimental design (e.g., A/B, multivariate, Bayesian methods) and incremental analysis.
- Experience working with large and multiple datasets/data warehouses and ability to pull from such data sets using relevant tools and coding.
About the job
Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.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.Responsibilities
- Build models and frameworks to understand the opportunity size of Customer Engineering (CE) initiatives. Enable informed decisions, contribute to annual and quarterly objectives and key results setting.
- Build an understanding of the large, complex data sets used by CE and our partner teams. Work with engineering teams to plug gaps in logging and data infrastructure.
- Build data aggregation and analysis pipelines, designing new metrics, and creating dashboards and visualizations around them.
- Determine metric definition for each workstream to ensure alignment/rollup to team and organization objectives and key results.
- Improve experimentation velocity and analysis turnaround time through adoption of self-service tools such as RASTA.
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