Business Data Scientist, Ads Marketing Analytics
Confirmed on the employer's own hiring board on Oct 21, 2026. First seen by Alion on Oct 15, 2026. Google scores B on the Alion truth index.
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.
Google Ads Marketing helps advertisers of all sizes succeed with digital marketing. In this role, you will work with a team to advance marketing science for customers using Google’s advertising solutions. This unique opportunity applies Data Science tools to accelerate Ads business growth, working cross-functionally with Sales, Marketing, and Product teams.
As a Data Scientist on this team, you will perform deep data analytics, drive experimentation and measurement, and advance machine learning modeling to support global marketing programs.
Collaborating with a multidisciplinary team of marketers, product managers, data scientists, and engineers, you will leverage underlying data to align on key metrics and methodologies. Your insights will enable marketers to develop highly effective programs. Using core Data Science expertise, you will design, prototype, and build scalable analysis pipelines to support campaigns. You will perform analytics, execute experimentation, and conduct incrementality measurement to inform strategic decisions across the entire Ads Marketing lifecycle-from acquisition and onboarding to growth and retention.
You will build investigative frameworks and measurement capabilities to generate data-driven insights that drive business growth. You will effectively communicate your investigative results to marketing partners and leadership to inform critical decision-making.
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
- Work with large, complex data sets. Solve analysis problems, applying advanced investigative methods (such as statistical and machine learning models) as needed. Conduct analysis that includes problem formulation, data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Design and analyze controlled experiments or counterfactual causal inference studies to examine the incremental impact of Ads marketing programs.
- Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed.
- Interact cross-functionally, making business recommendations (e.g. cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
- Develop and automate reports, iteratively build and prototype dashboards to provide insights at scale, solving for business priorities.
Qualifications
Minimum qualifications:
- Master's degree in a quantitative field (Statistics, Mathematics, Data Science, Bioinformatics, Economics, etc.) or equivalent practical experience
- 3 years of experience in a data science field.
- Experience with statistical software (e.g., R, Python, MATLAB) and database languages (i.e., SQL).
- Experience using analytics to solve product or business problems, querying databases or statistical analysis.
Preferred qualifications:
- PhD in Statistics or related quantitative discipline.
- 2 years of experience, including statistical data analysis such as generalized linear models, multivariate analysis, clustering/segmentation and sampling methods.
- Experience in controlled experiment design and causal inference methods.
- Ability to prioritize requests and partner well in an environment with competing demands from stakeholders.
- Ability to convince business stakeholders and communicate analysis insights to non-technical audiences and willingness to both teach others and learn new techniques.
- Excellent communication and team-work including problem-solving skills.