Senior Data Scientist, Product, Google Play

In this role, you will partner with Product Manager (PM), engineer, User Experience (UX) and cross-functional teams to shape the product narrative and build or launch features.The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.

Responsibilities

• Perform analysis utilizing related tools (e.g., SQL, R, Python). Help solve problems, narrow down multiple options into the best approach, and take ownership of open-ended business problems to reach a solution.
• Build new processes, procedures, methods, tests, and components to anticipate and address future issues.
• Report on Key Performance Indicators (KPIs) to support business reviews with the cross-functional/organizational leadership team. Translate analysis results in business insights or product improvement opportunities.
• Build and prototype analysis and business cases to provide insights. Develop knowledge of Google data structures and metrics. Advocate for changes needed for product development.
• Collaborate across teams to align resources and direction.

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field, or equivalent practical experience.
• 8 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 5 years of experience with a Master's degree.

Preferred qualifications:

• Master's degree in Statistics, Machine Learning, Data Science, Economics, or a related quantitative field.
• Experience with developing machine learning models, launch experiments (e.g., A/B Testing), and end-to-end data infrastructure and analytics pipelines.
• Experience in developing new models, methods, analysis and approaches.
• Experience with classification and regression, prediction and inferential tasks, training/validation criteria for Machine Learning (ML) algorithm performance.
• Experience in identifying opportunities for business/product improvement and defining the success of initiatives.
• Ability to manage problems, with excellent communication and presentation skills to deliver findings of analysis.