Business Data Science Manager, Global Business Strategy and Operations

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.

Global Business Strategy and Operations (GBS&O) is part of the Go-to-Market organization making sure Google's business pursues the strategy and executes. GBS&O architects the future of Google’s global ads business, consisting of pragmatic and investigative and experts in business operations. We build creative ML and Data Science solutions to drive strategy and business impact. We help our organization make tough, smart, and difficult decisions to ensure the Ads Business continues to thrive.

In this role, you will oversee advanced data science and causal inference initiatives that influence core business strategies. Your team will solve business problems across channel architecture optimization, global business organization (GBO) ads policy analytics (identifying business growth and policy enforcement opportunities), and CustomerGeist customer sentiment analytics. By partnering closely with global sales leadership, finance, and strategy teams, you will build econometric models and causal inference frameworks that unlock scalable commercial insights and fundamentally elevate GBO's analytical excellence.

You will design and lead analyses and build data infrastructure that drive major decisions across GBO. You will develop your team into a global center of excellence that elevates GBO's excellence, translating data foundation and insights into transformative commercial strategy.The US base salary range for this full-time position is $192,000-$278,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Responsibilities

• Manage and mentor a team of Business Data Scientists, fostering a culture of experimentation, excellence, and collaboration within GBS&O Data, Insights, and Analytics (DIA). Act as a Leader within GBS&O driving excellence across the team, and within GTM.
• Oversee the development and global scaling of investigative solutions, such as causal inference, experimentation, forecasting, and data infrastructure to drive crucial decisions.
• Act as a thought partner to executive GTM, Finance, and StratOps Executives. Translate business problems into data-driven frameworks that guide executive decision-making and actionable commercial strategies (such as sales incentive designs, optimal ads policies, and resource allocation frameworks).
• Direct the end-to-end lifecycle of internal investigative products from problem identification to production deployment.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 7 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  
• 3 years of people management experience building, mentoring, and scaling technical teams of data scientists or engineers.
• Experience in SQL, and Python.

Preferred qualifications:

• 9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
  
• 8 years of experience in data science, advanced analytics, or business intelligence within enterprise or tech environments.
• 4 years of experience as a people manager within a technical leadership role.
  
• Proficiency in SQL, Python/R, data warehouse architecture (e.g., BigQuery), and data transformation pipelines.
• Track record developing predictive propensity models and causal inference frameworks (A/B testing, incrementality).
• Excellent communication and stakeholder management skills, with demonstrated success partnering with executive leadership teams.