Minimum qualifications:

• Bachelor’s degree in Data Science, or similar technical field of study, or equivalent practical experience.
• 15 years of experience in data science or other quantitative fields such as: statistics, machine learning, software engineering, or ML engineering.
• Experience in using data insights to drive product and engineering excellence.

Preferred qualifications:

• Master's degree or PhD in Data Science, Statistics, or equivalent.
• Experience building 0 to 1 products as well as working on established products.
• Strong leader able to produce great outcomes through influence rather than authority.
• Innovative problem solver who turns ambiguity into clear solutions, and comfortable with AI technology and out-of-the-box thinking.

About the job

As we enter a new generation of technology, users are quickly updating their mental models of how Google Search can help them in their lives. As a Principal Data Scientist on Search, you will drive understanding of this rapidly evolving voice of the user to help solve complicated, broad problems that are existential to the future of Search in terms of both quality and experience. This role will vary in terms of “core scope” and type of problem, but will have the opportunity and expectation of branching off of this core scope to help us determine the future of Search.

In this role, you will use your data brain to establish deep understanding, and use this understanding in direct collaboration with executive cross-functional partners to build systems and products that will change the world.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

Responsibilities

• Build a strong measurement framework for broadly defined problems so we can “hill-climb” by orienting our efforts in the correct direction.
• Identify and prioritize the most important levers to add user value across a variety of surfaces and things we could build.
• Establish standards for depth of understanding and accuracy for the Search Data Science organization.
• Build operating pictures for important cross-cutting areas (such as what a successful AI experience looks like), and align the organization around the ensuing clarity and expertise.