Staff Product Data Scientist, Workspace Monetization

Google•Kirkland, WA

64d•$192,000 - $279,000

About The Position

Google Workspace is a smart, simple, and secure suite of productivity apps—including Gmail, Docs, Drive, Calendar, Sheets, Meet, and Chat. Designed to simplify workflows and boost team productivity, Workspace features real-time collaboration at its core, allowing information to flow freely across devices and teams so that great ideas are never lost. As a Staff Data Scientist on the Workspace Monetization Product team, you will play a key role in growing the business and impacting millions of users worldwide. In this role, you will partner with product and engineering teams to shape business generation and growth strategies. You are a technically proficient data scientist, a thinker, and a compelling communicator who can operate separately to influence product roadmaps through data-driven insights.

Requirements

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

Nice To Haves

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

Responsibilities

Act as a thought partner to Engineering and Product Management leads, providing data-driven perspectives on product direction and opportunities.
Conduct in-depth search analyses to identify the most significant opportunities for growing Workspace business and user value.
Communicate complex findings and recommendations clearly and effectively to technical and non-technical stakeholders, including executive leadership.
Own project outcomes by covering problem definition, metrics development, data extraction and manipulation, visualization, and the implementation of statistical models. Oversee the contributions of others and develop colleagues’ capabilities within your area of specialization.
Lead and manage problems that may be ambiguous or lack clear precedent by framing hypotheses and making recommendations that combine problem-solving and product-specific expertise.