Business Data Scientist, Subscriptions and Customer Growth Marketing

Work with large, complex data sets, applying advanced analytical methods to 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. 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.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.Minimum qualifications:Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.Experience building and deploying machine learning models, with practical application in recommendation engines or customer segmentationPreferred qualifications:4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.Experience in controlled experiment design and causal inference methods.Applied experience with machine learning on large-scale computing systems like Hadoop, MapReduce or similar environments. Expertise with statistical data analysis such as generalized linear models, multivariate analysis, clustering/segmentation, and sampling methods.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.Know the user. Know the magic. Connect the two. Google Marketing starts with technology and ends with the user, bringing them together unconventionally. We approach marketing by demonstrating how our products solve problems—from the everyday to the epic—changing the game, redefining the medium, prioritizing the user, and letting the products speak for themselves.The Subscriptions and Customer Growth Marketing organization drives consumer apps and subscription growth. We partner with product engineering and insights to understand the user and bring helpful products to market while deepening the consumer relationship. We’re passionate about showing consumers how to get more out of their favorite Google subscriptions and consumer apps.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $138000 - $197000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; San Francisco, CA, USA.Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.Experience building and deploying machine learning models, with practical application in recommendation engines or customer segmentation