Business Data Scientist, Ads Marketing Analytics

Minimum qualifications:

• Master's degree in Statistics, Mathematics, Bioinformatics, Economics, a related field, or equivalent practical experience.
• 2 years of experience in a data science field.
• Experience with statistical software (e.g., R, Python, MATLAB) and database languages (i.e., SQL).
• Experience leveraging data insights into storytelling for business stakeholders.

Preferred qualifications:

• PhD in Statistics or a related field.
• 2 years of experience with statistical data analysis such as generalized linear models, multivariate analysis, clustering/segmentation, and sampling methods.
• Experience with machine learning on large-scale computing systems like Hadoop, MapReduce, or similar environments.
• Experience in controlled experiment design and causal inference methods.
• Ability to prioritize requests and partner well in an environment with competing demands from stakeholders.
• Excellent communication skills.

About the job

Google Ads Marketing aims to help advertisers of all sizes succeed with digital marketing. In this role, you will work with a team to advance the science of Marketing to customers that use Google’s advertising solutions. This is a unique opportunity to apply the tools of Data Science to accelerate Ads business growth, working cross-functionally with Sales, Marketing, and Product teams. You will perform data analytics, drive initiatives in experimentation, measurement, and advance machine learning modeling capability to support global marketing programs. In collaboration with a multidisciplinary team of Marketing, Product Management, Data Scientists, and Engineers, you will tap into the underlying data, develop and align on key metrics/methodologies, and generate insights that enable marketers to develop powerful, highly effective marketing programs.

You will leverage core Data Science expertise to design, prototype, and build out analysis pipelines to support initiatives and Marketing campaigns at scale. You'll perform analytics, design and execute on experimentation, and conduct incremental measurement analysis to inform strategic decisions of the marketing programs across the entire Ads Marketing space, from acquisition, onboarding, growth, and retention. You will build analytical frameworks and measurement capabilities to generate data driven insights that drive business growth. You will present and communicate effectively to marketing partners and leadership, data-driven insights and analytic results to inform on decision making.

The US base salary range for this full-time position is $124,000-$182,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. 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

• Work with large, complex data sets. Solve complex analysis problems, applying advanced problem-solving 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.