Responsibilities

• 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.

Requirements

• 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

Preferred

• 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.