Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 11 years of experience in management consulting, product management and strategy, or analytics in a technology company.
- Experience working with and analyzing data, and managing multiple cross-functional programs or projects.
Preferred qualifications:- 8 years of experience solving business problems using Python, R, SQL, and advanced statistical analysis to extract executive-level insights from complex datasets.
- 5 years of experience driving performance advertising strategies and translating dense technical findings into high-impact presentations for executive audiences.
- Understanding of user behavior patterns, performance metrics, and post-deployment operational challenges unique to generative AI products.
- Ability to lead initiatives across disparate business units and establish operational cadences to keep teams accountable.
About the jobIn this role, you will act as the analytical engine for the GenAI Product organization. Our core mission is transitioning the organization from short-term launch tracking to continuous, data-informed product lifecycle management. Embedded within the GenAI Product Strategy and Operations team, you will act as the primary bridge between raw data, cross-functional analytical working groups, and executive product strategy across our entire product suite. You will add a critical layer of investigative excellence-getting under the hood of the data to validate metrics, uncover hidden trends, and proactively delight product leadership with strategic insights.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $236000 - $257000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Track and investigate product landings, establishing operating rhythms for post-launch performance, adoption, and tracking.
- Capitalize on macro-level dashboards to chase ambiguous trends, answer complex questions, and pivot wide-angle data into actionable insights.
- Use Business Intelligence tools and SQL to identify behavioral shifts, usage anomalies, and performance gaps across product surfaces.
- Partner with cross-functional metrics working groups, Technical Program Managers, and Data Scientists to audit anomalies, test hypotheses, and translate product questions into technical data requirements.
- Build deep relationships with embedded data analysts across neighboring product areas to drive joint technical investigations and align on unified GenAI metrics.
