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.

As a Staff Business Data Scientist, you will serve as a full-stack technical lead, owning the end-to-end life-cycle of data science products that drive Google Cloud’s marketing and Go-to-market (GTM) strategy and measurement. You will move beyond traditional analysis to architect and build scalable intelligence systems.

In this role, you will bridge the gap between data engineering and data science. You will build the infrastructure required to ingest and process massive datasets, develop predictive models (e.g., lead scoring, propensity predictions), and engineer the Application Programming Interfaces (APIs) or serving layers that integrate these insights directly into our marketing measurement and tech stack. You will have a specific mandate to leverage Google’s Generative AI capabilities and will utilize Large Language Models (LLMs) and Gemini models to engineer novel data science products that enhance our predictive capabilities. You will advocate for software engineering best practices within the data science team, ensuring our code is testable and maintainable. You will work with Marketing leadership to ensure the intelligence systems you build actively influence decision-making. You will also mentor data scientists on the team and advocate for statistical methodology and coding standards across the organization.

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.

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

• Lead the full Machine Learning (ML) life-cycle from data extraction and feature engineering to model training, validation, and deployment for critical marketing capabilities. Design and build ML models that solve ambiguous business problems and optimize the full customer life-cycle and demand funnel.

• Design scalable data science applications using Google’s LLM models to unlock insights from structured and unstructured data, build intelligent marketing agents, and automate decision-making processes within the Business-to-Business (B2B) funnel.

• Define coding standards and engineering best practices for the team; mentor other data scientists on writing production-quality code and designing scalable architectures.

• Partner with engineering and cross-functional data science teams to integrate model outputs directly into our martech systems, ensuring insights drive automated action.

• Translate data science outputs into clear, actionable business recommendations for Director and Vice President level stakeholders.

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.

• Experience deploying ML models into production environments.

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.

• Experience with Machine Learning Operations (MLOps) tools and practices.

• Understanding of business-to-business (B2B) enterprise SaaS business cycles, demand generation funnels, and marketing technology stacks.