Senior Business Data Scientist, YouTube Marketing
GoogleAbout the role
Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
Preferred qualifications:
- 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
About the job
Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.
At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems--from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can--changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.
YouTube (YT) Marketing Data Science, Infra and Analytics (DSIA) influences and informs YouTube’s marketing and products teams. We work on projects that drive a go-to-market and brand strategy for YouTube using data to ensure our marketing efforts are efficiently and effectively deployed.
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
- Develop metrics to track and evaluate solution deployment across teams.
- Create dashboards and tools to automate processes, generate reports, and guide product decisions.
- Curate and validate data to ensure quality standards are met.
- Collaborate with stakeholders to understand the domain, business goals, and data infrastructure context.
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