Minimum qualifications:
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related field or equivalent practical experience.
- 10 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 8 years work experience including a Master's degree).
- 3 years of experience as a people manager within a technical leadership role.
Preferred qualifications:
- Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
- 12 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).
- 4 years of experience as a people manager within a technical leadership role.
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.The Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google’s artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.
Responsibilities
- Manage and work with team members to track and verify software improvements.
- Design and set up systems to detect system-level issues and anomalies in our consumer populations across many different metrics such that we can detect problems before they affect our millions of users.
- Monitor our products over time to make sure new and changing applications, user behavior, and our own hardware do not cause any unanticipated regressions.
- Validate that new software releases enhance customer experiences, ensuring value-added features remain within allocated budgets and do not compromise performance.
- Optimize the utilization of metrics to enhance data-driven decision-making.
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