Business Data Scientist, AI Foundations

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 Foundations Finance team is at the epicenter of Google's transition to a Cloud-Native, AI-first enterprise. We provide dedicated financial stewardship, quantitative modeling, and technical program leadership for the modernization of Google's internal technical infrastructure and corporate workflows. Our mission is to architect the economic frameworks and data foundations that accelerate Google’s Tech Transformation and power the velocity of the entire company.

As the centralized Business Data Scientist on this team, you will serve as our quantitative modeling, telemetry, and analytics architect across all AI Foundations Finance pillars. Working closely with Finance and Engineering leadership and partnering directly with our Technical Program Managers (TPMs), you will build the unified data foundations, predictive forecasts, and Return on Investment (ROI) frameworks that guide executive decision-making across cloud-native developer velocity, hybrid cloud interoperability, next-generation agent infrastructure, and Frontier AI data investments.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google . Responsibilities

• Develop predictive models to optimize business processes and drive strategic decision-making.

• Analyze large datasets to extract actionable insights and identify new growth opportunities.

• Translate complex data findings into clear recommendations for business stakeholders and executive leadership.

• Design key performance indicators to measure the business impact of AI foundation models.

• Formulate data-driven strategies to improve customer engagement and lifetime value metrics.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.

• 3 years of experience with AI products, technologies, and infrastructure.

• Experience with statistical modeling, causal inference, A/B testing, or experimental design.

• Experience with regression analysis for prediction and forecasting.

Preferred qualifications:

• Experience in applied data analytics, including designing automated dashboards, data visualizations, and quantitative models to solve business or engineering problems.