Data Scientist, Enterprise Platform
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Minimum qualifications:
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 2 years of experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or experience with a Master's degree).
Preferred qualifications:
- Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 3 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL).
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.
To accelerate the growth and market leadership of Enterprise Buying Platforms (DV360 and SA360) by answering critical business questions and delivering actionable, data-driven insights that inform product and commercial strategy. The Enterprise Platform Data Science Team provides quantitative support, market understanding and a strategic perspective to our partners throughout the organization, in close collaboration with the Ads and Commerce Finance team.
Responsibilities
- Execute defined, moderately difficult investigative tasks under guidance from the manager or executive team member/team lead. For straightforward problems, execute end-to-end analysis with minimal guidance.
- Manage workload to reflect the priorities set by the team, work towards a timeline, and communicate slippage.
- Select appropriate approaches from clear options to address technical challenges under some guidance from managers or executive team members. Plan out analyses (as opposed to trial and error approach).
- Break down broader tasks into components and anticipate complexities/blockers.
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