About the job

Google's leadership team hand-picks thorny business challenges, and members of Biz Ops 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 Google Cloud Marketing Data Science and Analytics (DSA) team is responsible for end-to-end development of data assets, data science and ML capabilities, and analytics and insights for Google Cloud Marketing. DSA is the strategic core of our marketing organization. Our mission is to empower Google Cloud's growth by transforming raw data into actionable intelligence. We leverage cutting-edge data science, machine learning, and analytics to decode and predict customer behavior, measure the impact of marketing motions and programs, and optimize every facet of our marketing strategy to accelerate Google Cloud's growth.

As the Head of Data Science for Google Cloud Marketing, you will define and lead the strategic roadmap for data science and machine learning to accelerate our B2B go-to-market (GTM) motions. You will lead a team of data scientists to build the intelligence engine that powers how we identify, engage, and convert customers globally.

The US base salary range for this full-time position is $240,000-$334,000 + 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.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Define and execute the long-term vision for data science within Cloud Marketing, building scalable ML/AI capabilities that optimize the B2B customer journey.

• Oversee the development and deployment of robust AI-native data science capabilities and predictive models to accelerate marketing motions and personalize marketing outreach at scale. Deploy intelligent agents to automate experiences and drive engagement across various channels.

• Lead the creation of sophisticated attribution models and incrementality experiments to quantify marketing impact, optimize media spend, and unlock insights into funnel performance.

• Partner with GTM, sales operations, and engineering leadership to integrate data science capabilities into operational workflows (e.g., CRM systems), ensuring high adoption and alignment with business goals.

• Build, mentor, and lead a high-performing team of data scientists, fostering a culture of technical excellence, innovation, and thorough statistical methodology.

Minimum qualifications

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

• 9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

• Experience managing and developing teams of data scientists.

• Experience building and deploying machine learning models for business applications (e.g., lead scoring, attribution, forecasting).

Preferred qualifications

• 11 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

• 6 years of experience as a people manager within a technical leadership role.

• Experience with cloud computing platforms (e.g., Google Cloud Platform, Big Query, Vertex AI) and handling large-scale datasets.

• Deep understanding of the B2B enterprise SaaS marketing and sales cycle, demand generation funnels, and marketing technology stacks (e.g., Salesforce, Marketo).