Data Scientist, Product, Google Enterprise

Minimum qualifications:

• Master's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
• 3 years of experience with statistical data analysis, data mining, and querying (e.g., SQL).
• 3 years of experience managing analytical projects.

Preferred qualifications:

• Master's degree in machine learning, statistics, a related field, or equivalent practical experience.
• Experience applying ML and advanced analytics solutions to enterprise operations teams.
• Experience with the full ML product lifecycle, from ideation to exploration, productionization, and long term support.

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

In this role, you will transform Google Enterprise business operations, supply chain, IT support, and internal tooling with AI and Advanced Analytics. You will also enable operations and product teams to succeed in their advanced analytics projects through the use of differing engagement models, ranging from consulting to productionizing and deploying models.

• Provide analytical thought leadership through proactive and strategic contributions, use insights and analytics to drive decisions and alignment throughout the organization.
• Work in a team covering problem definition, metrics development, data extraction and manipulation, visualization, creation, implementation of analytical/statistical models, and presenting to stakeholders.
  
• Define and report Key Performance Indicators (KPIs) and launch impact as part of regular business reviews with the cross-functional and cross-organizational leadership team.
• Design and develop machine learning models to solve problems within the Enterprise Support space that solve business problems for Google, and work alongside ML Engineering teams to test and deploy generative AI models to internal customers. 
  
• Review analysis and business cases conducted by others to provide feedback.