Staff Data Scientist, Product, Google Play
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Minimum qualifications:
- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 7 years of experience with statistical data analysis, modeling, experimentation, causal inference, data mining, querying, and managing analytical projects with programming in SQL, Python, or R.
- 7 years of experience developing and managing metrics or evaluating programs/products.
- 3 years of experience as a people manager within a technical leadership role.
Preferred qualifications:
- 7 years of experience in scripting, statistical analysis (e.g., R, Stata, SPSS, SAS), developing and managing metrics, and evaluating programs/products.
- 7 years of experience working in a complex, matrixed organization.
- 7 years of experience preparing and delivering technical presentations to executive leadership.
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.
In this role, you will work on components of analytics, strategies and communications. Our team partners with senior leadership to run important functions that cross-cut our existing organizations and deliver high impact projects.
Responsibilities
- Identify and structure business problems, communicate challenges and opportunities.
- Act as a thought partner to produce insights and metrics for various technical and business stakeholders.
- Deliver effective presentations of findings and recommendations to multiple levels of leadership, creating visual displays of quantitative information.
- Follow engineering best practices to create scalable data pipelines and models.
- Engage and influence senior stakeholders and cross-functional partners, obtain buy in for potential solutions, adapt messaging and suggested recommendations.
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