Minimum qualifications:

　　Master's degree in Statistics, Mathematics, Bioinformatics, Economics, a quantitative 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 (e.g. SQL).

　　Experience leveraging data insights into storytelling for business stakeholders.

　　Preferred qualifications: PhD in Statistics or a related quantitative discipline.

　　2 years of experience in statistical data analysis (e.g., generalized linear models, multivariate analysis, clustering or segmentation, and sampling methods).

　　Experience in controlled experiment design and causal inference methods.

　　Experience with machine learning on computing systems (e.g., Hadoop, MapReduce, or a similar system).

　　Ability to prioritize requests, teach others, learn techniques, partner in a environment with competing demands, collaborate with stakeholders, and communicate analysis insights to non-technical audiences.

　　Ability to take initiative with excellent leadership and communication skills.

　　About the job

　　As a Business Data Scientist, you will perform data analytics, lead initiatives in experimentation and measurement, and advance machine learning modeling capability to support global marketing programs.

　　In collaboration with multidisciplinary teams, you will tap into the underlying data, develop and align on metrics or methodologies, and generate insights that enable marketers to develop marketing programs. You will design, prototype, and build out analysis pipelines to support initiatives and Marketing campaigns at scale. You will perform analysis, design, and execute on experimentation, and conduct incrementally measurement analysis to inform on strategic decisions of the marketing programs across the entire Ads Marketing space.

　　In this role, you will build analytical frameworks and measurement capabilities to generate data driven insights that lead business growth. You will present and communicate to marketing partners and leadership to inform on decision making.

　　Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems--from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can--changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.

　　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 and solve complex analysis problems, applying advanced analytical methods (e.g., statistical and machine learning models). 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.