Staff Business Data Scientist, Google Cloud Marketing
Google · New York, NY
This listing has closed — view similar roles below.
- On site
- Full-time
- $278,000 / year
- New York, NY
Job highlights
- Lead end-to-end data science products for Google Cloud marketing.
- Build scalable intelligence systems and predictive models.
- Leverage Generative AI and LLMs for novel data products.
- Collaborate with engineering and marketing leadership.
- Mentor data scientists and advocate for best practices.
About the role
About The Job
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.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.
Key skills/competency
- Machine Learning
- Data Science
- Python
- SQL
- Data Engineering
- Generative AI
- LLMs
- Marketing Technology
- B2B SaaS
- Statistical Analysis
Benefits for eligible US based employees
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle)
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Skills & topics
- Staff Business Data Scientist
- Data Science
- Machine Learning
- Python
- SQL
- Data Engineering
- Generative AI
- LLMs
- Marketing Technology
- B2B SaaS
- Google Cloud
- Predictive Modeling
- Analytics
- Statistical Analysis
- MLOps
How to get hired
- Tailor your resume: Highlight your 7+ years of experience in analytics, coding (Python, R, SQL), and deploying ML models, aligning with Google's requirements for Staff Business Data Scientist.
- Showcase MLOps and B2B SaaS expertise: Emphasize any experience with Machine Learning Operations and understanding of B2B enterprise SaaS business cycles, demand generation funnels, and marketing technology stacks.
- Quantify your impact: Provide specific examples of how your data science solutions have solved product or business problems and driven measurable results.
- Prepare for technical and behavioral interviews: Be ready to discuss your experience with full-stack data science, LLMs, Generative AI, and your ability to mentor others and communicate with senior stakeholders.
- Research Google's culture: Understand Google's commitment to innovation, data-driven decision-making, and their specific approach to marketing and cloud services.
Technical preparation
Master advanced ML algorithms and their applications.,Practice coding in Python, R, and SQL extensively.,Build and deploy ML models in production environments.,Familiarize yourself with MLOps and Generative AI.
Behavioral questions
Describe a complex business problem you solved.,How have you mentored junior data scientists?,Explain a time you influenced senior stakeholders.,How do you ensure code quality and scalability?
Frequently asked questions
- What are the minimum qualifications for the Staff Business Data Scientist role at Google Cloud Marketing?
- The minimum qualifications include a Master's degree in a quantitative discipline or equivalent practical experience, 7 years of experience in analytics, coding (Python, R, SQL), querying databases, or statistical analysis, and experience deploying ML models into production environments.
- What preferred qualifications would make a candidate stand out for the Staff Business Data Scientist position?
- Preferred qualifications include 9 years of experience in analytics, coding, querying, or statistical analysis, experience with Machine Learning Operations (MLOps) tools and practices, and a strong understanding of business-to-business (B2B) enterprise SaaS business cycles, demand generation funnels, and marketing technology stacks.
- What is the typical work arrangement for this Staff Business Data Scientist role at Google?
- Candidates have the opportunity to select their preferred working location from Seattle, WA, USA; Kirkland, WA, USA; New York, NY, USA; or Sunnyvale, CA, USA, suggesting a hybrid or on-site arrangement depending on the chosen location.
- Can you provide more details on the salary for the Staff Business Data Scientist position at Google?
- The US base salary range for this full-time position is $192,000-$278,000. This range does not include bonus, equity, or benefits, which are also part of the comprehensive compensation package. The exact salary will depend on factors like work location, skills, experience, and education.
- What kind of projects will a Staff Business Data Scientist work on at Google Cloud Marketing?
- Staff Business Data Scientists will lead the full ML life-cycle for marketing capabilities, design and build ML models for customer life-cycle optimization, design data science applications using LLMs and Generative AI, and integrate model outputs into martech systems to drive automated actions.
- What is Google's approach to employee benefits for this role?
- Google offers a comprehensive benefits package for eligible US-based employees, including health, dental, vision, life, and disability insurance, 401(k) with company match, generous paid time off, sick time, maternity leave, baby bonding leave, and paid holidays.
- How does Google Cloud Marketing leverage Generative AI and LLMs in this role?
- The role has a specific mandate to leverage Google's Generative AI capabilities, including Large Language Models (LLMs) and Gemini models, to engineer novel data science products that enhance predictive capabilities and unlock insights from structured and unstructured data.