Data Scientist, Google Cloud Support
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Minimum qualifications:
- Bachelor's degree in Computer Science, Information Systems, a related technical field, or equivalent practical experience.
- 3 years of experience developing/deploying machine learning and time series forecasting models using statistical software (e.g., Python, R).
- 3 years of experience applying statistical modeling, hypothesis testing, and experimentation.
- 3 years of experience analyzing data, working with SQL and databases.
Preferred qualifications:
- Master's degree with a quantitative focus (e.g., Computer Science, Data Science, Mathematics, Economics, Physics, Engineering, Management Information Systems, Statistics, Accounting), or other relevant field.
- Experience in product analytics, customer support, business intelligence, data science, or data warehousing.
- Ability to collaborate with internal stakeholders across different functions and geographies to manage change and drive continuous improvement initiatives.
- Excellent communication skills and executive presence to influence Google colleagues and manage business priorities.
About the job
As part of the Cloud Support Data Science team, you will play a key role in using data and machine intelligence to empower data-driven execution of strategy and operations for Google customers. We work collaboratively with Sales, Engineering and other Cloud Support teams to build analytics solutions that enable actionable insights to provide an effortless customer experience.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Build and maintain data pipelines and time series models to generate support case volume forecasts that enable long-term capacity planning and short-term scheduling decisions.
- Lead monthly business reviews with executive stakeholders, sharing insights on drivers of change across a dynamic organization.
- Engage broadly with the organization to identify, prioritize, frame, and structure complex and ambiguous challenges, where advanced analytics projects or tools can have the biggest impact.
- Help define the direction for the team, and influence the direction of the associated engineering and infrastructure work.
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