Staff Business Data Scientist, Google Labs and Workspace

Staff Business Data Scientist, Google Labs and Workspace Share Staff Business Data Scientist, Google Labs and Workspace _corporate_fare_ Google _place_ Mountain View, CA, USA; San Francisco, CA, USA; +2 more; +1 more **Mid** Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. Share Staff Business Data Scientist, Google Labs and Workspace _info_outline_ XApplicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act. In most instances, this position requires in-person interviews as part of the hiring process.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: **Mountain View, CA, USA; San Francisco, CA, USA; New York, NY, USA** . **Minimum qualifications:**

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• 4 years of experience building agentic analytics workflows, custom skills, or automated pipelines.
• 4 years of experience in SQL and Python or R for data modeling, cohort analysis, randomized controlled trials (A/B testing), holdback experiments, causal inference/incrementality models, and workflow automation.
• 4 years of experience in growth analytics, marketing data science, or product analytics.

**Preferred qualifications:** + 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis. **About the job** Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations. The AI Growth Engine team across Google Labs and Google Workspace drives full-funnel user acquisition, onboarding, lifecycle activation, and business generation for Google's breakthrough AI products and Google Workspace with Gemini. Operating as an AI-native growth pod, we combine rigorous experimentation, product telemetry, and agentic automation to turn early product innovation into sustained global adoption. As a Growth Data Science Analyst for AI Growth, you will build the investigative intelligence, experimentation backbone, and data pipelines powering user acquisition, activation, and monetization across Google Labs and Google Workspace. Embedded within an AI-first growth team, you will bridge product telemetry, web analytics, and omnichannel activation—writing production SQL/Python and building agentic data skills to turn raw user signals into automated audience cohorts and closed-loop incrementality readouts in minutes. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits Learn more aboutbenefits at Google ( . **Responsibilities**

• Architect and maintain unified full-funnel growth data pipelines and executive dashboards tracking acquisition, activation, adherence across Labs and Workspace products.
• Build and automate dynamic first-party audience cohorting tables to power real-time lifecycle and remarketing triggers in Google Notification Platform, Chime mobile push, and Audience Builder.
• Audit and validate end-to-end event instrumentation and conversion tracking pipelines—identifying and fixing data discrepancies between product logs, GA4, and Google Ads conversion imports.
• Design, size, and analyze rigorous A/B, multi-cell, and geo-experimentation studies to measure true incremental lift across paid media, customer relationship management (CRM) lifecycle, and site optimization initiatives.
• Develop reusable AI/agentic analytics workflows, model context protocol (MCP) tools, and automated anomaly-detection scripts that enable the growth team to self-serve cohort sizing and campaign diagnostics at machine speed.

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