Minimum qualifications:
- Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
- 5 years of experience with statistical data analysis, data mining, and querying (e.g., SQL).
- 3 years of experience managing analytical projects.
- 5 years of experience in scripting, statistical analysis (e.g., R, Stata, SPSS, SAS), developing and managing metrics, and evaluating programs/products.
- Experience with the full ML product lifecycle, from ideation to exploration, productionization, and long term support.
- Experience building production ML pipelines
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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 help develop data-leveraged solutions that optimize the operational efficiency of internal stakeholder teams who equip users with the personal and collaboration technology they need to be productive. The team drives awareness with internal customers throughout the Corporate Engineering organization about how to effectively manage and use data for strategic advantage, model critical business operations, and deploy AI/ML solutions. The team also provides quantitative support and are key strategic partners to the business by delivering data-driven insights and solutions.
Responsibilities
- Perform analysis utilizing tools (e.g., SQL, R, Python), help solve problems, narrow down multiple options into the best approach, and take ownership of open-ended ambiguous business problems to reach an optimal solution.
- Report on Key Performance Indicators (KPIs) to support business reviews with the cross-functional/organizational leadership team.
- Build and prototype analysis and business cases to provide insights, develop comprehensive knowledge of Google data structures and metrics, and advocate for changes needed for product development.
- Design and develop machine learning models to solve problems within the Enterprise Support space, and work alongside Machine Learning Engineering teams to test and deploy generative AI models.
- Collaborate with Engineering teams to identify and address instrumentation gaps, ensure accurate data collection for key functionalities, and focus on impactful customer journeys.