Data Scientist, Product, Google Play
Job Summary
Job Description
- Master's degree in quantitative field
- 3+ years data analysis experience
- 3+ years project management experience
- SQL, R/Stata/SPSS/SAS proficiency
- Data pipeline & model building
- Excellent communication skills
- 5+ years experience in scripting and statistical analysis
- Experience in a matrixed organization
- Experience presenting to executive leadership
3 skills required
Job Details
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).
- 3 years of experience managing analytical projects.
Preferred qualifications:
- 5 years of experience in scripting, statistical analysis (e.g., R, Stata, SPSS, SAS), developing and managing metrics, and evaluating programs/products.
- 3 years of experience working in a complex, matrixed organization.
- 3 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.
Responsibilities
- Identify and structure business problems, and 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 executive stakeholders and cross-functional partners, obtain buy-in for potential solutions, and adapt messaging and suggested recommendations.
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