Staff Data Scientist, Product, Corporate Engineering

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 10 years of 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.

Preferred qualifications:

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

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.

The Business Platform for Sales and Support (BPSS) team is part of Enterprise Platforms and Ecosystems (EPE) within Corporate Engineering (CorpEng), with a mission to build products for Google's internal contact center business for sales and support. We are a fast-moving, high-impact team that operates like a startup, with the resources of Google behind us. As a Data Scientist on this team, you will work closely with product and engineering teams throughout the entire development process to help build and shape next-generation GenAI products for our users.

• Define, own, and evolve product success metrics, as well as report, analyze, and forecast key product trends to make recommendations for improvement.
• Perform data exploration to understand user behavior and identify opportunities for improving products.
• Apply technical expertise in observational data analysis, modeling, and causal inference to answer product questions.
• Lead the design, analysis, and interpretation of product experiments to measure the causal effects of product changes.
• Partner with Product, Engineering, and other cross-functional teams to influence, prioritize, and support product strategy. This involves framing and solving ambiguous business problems, acting as a thought partner, influencing a wide range of product and engineering stakeholders.