About the job

The Growth and Notifications (GaNT) Data Science team's mission is to deliver the best of Google's products to our users by enabling high-quality notifications and driving growth across the ecosystem. We are a team of data scientists and analysts who work to embed a deep empirical understanding of user behavior into the product development lifecycle. This enables our product partners to take smarter risks and build more engaging, high-quality experiences that drive sustainable growth.

We are part of the broader Ecosystem Growth (EG) Data Science team within Google Identity and Engagement (GIE). Our work is pivotal in increasing the number of users who experience the full value of Google.

This is an exciting opportunity to join a team that is at the forefront of driving user engagement and ecosystem growth at Google.

Responsibilities

• Lead large-scale data analysis and novel modeling approaches to create a deep understanding of user behavior with notifications and proactive agentic experiences.

• Partner with Product, Engineering, and UX Research to define and develop novel evaluation metrics, validation methodologies, and experimentation frameworks for proactive quality models and AI-driven notification agents.

• Provide analytical thought leadership and advanced statistical expertise on user journeys, engagement models, and long-term causal effects across the GIE and Google ecosystem.

• Translate complex findings into actionable product recommendations, effectively presenting to cross-functional stakeholders at all levels to influence the product roadmap.

• Define, implement, and own key metrics to measure the quality, impact, and value of notifications and user-state models.

Minimum qualifications

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.

• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.

• Experience with statistical data analysis, experimental design (e.g., A/B testing), and causal inference.

• Experience in technical leadership.

Preferred qualifications

• PhD degree in Statistics, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.

• 3 years of experience applying advanced statistical and machine learning methods to solve business problems, using coding languages such as Python, R, or SQL.

• Experience in framing and solving unstructured business problems with data science, translating results into impactful business recommendations, and measuring the success of those initiatives.

• Experience with working with large-scale distributed datasets.

• Experience with AI evaluation techniques.

• Strong communication and presentation skills with experience presenting to cross-functional stakeholders.