Staff Data Scientist, Product — Drive Impact & Scale

Google LLC; Mountain View (CA); On-site; USD 192,000 - 278,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

401(k) match

Paid time off

Sick time

Maternity leave

Baby bonding

Paid holidays

Job summary

Google LLC is seeking a Data Scientist to serve as an analytics expert, turning data into strategic guidance for product and engineering teams. You will build quantitative models, communicate insights clearly, and influence key decisions across the organization.

The role emphasizes extensive programming in Python/R/SQL, a strong statistical background, and the ability to work across functions on scalable analytics projects. In-person interviews are common.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or related quantitative field.
• 10 years of analytics experience solving product or business problems, performing statistical analyses, and coding (Python/R/SQL) (or 8 years + Master's).
• 12 years of analytics experience with advanced quantitative methods and coding capabilities.

Responsibilities

• Perform analysis using relevant tools (e.g., SQL, R, Python).
• Develop analytical models and present findings to stakeholders.
• Lead cross-functional analytics initiatives to drive product and business improvements.
• Provide strategic analytics insights to engineering and product teams.

Skills

Analytics

Statistical analysis

Coding (Python/R/SQL)

Education

Bachelor's degree in a quantitative field

Master's degree in a quantitative field

Tools

SQL

R

Python

Job description

Google LLC is seeking a Data Scientist to serve as an analytics expert, turning data into strategic guidance for product and engineering teams. You will build quantitative models, communicate insights clearly, and influence key decisions across the organization.

The role emphasizes extensive programming in Python/R/SQL, a strong statistical background, and the ability to work across functions on scalable analytics projects. In-person interviews are common.