Business and Marketing Data Scientist

The US base salary range for this full-time position is $138,000 - $198,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Position reports to the Google Atlanta, GA office & may allow for a hybrid schedule as per Google policy.

Responsibilities

• Oversee the gathering, extraction, and validation of data from diverse sources to ensure quality and security standards are met.
• Collaborate with stakeholders to define business goals, data requirements, and analytical roadmaps for key projects.
• Formulate and interpret complex data to derive actionable conclusions and guide strategic business decisions.
• Create automated dashboards, tools, and reports to monitor performance, guide product decisions, and streamline processes.
• Develop and implement metrics to track and evaluate the deployment and impact of data-driven solutions across teams. Contribute production-level solution code and provide technical expertise to identify product gaps and mentor other teams.

Minimum qualifications:

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics or a related field and 5 years of progressive post-baccalaureate experience in the job offered or in a Business Data Science-related occupation.
• Alternatively, will accept a Master’s degree in Data Science, Statistics, Computer Science, Mathematics or a related field, and 3 years of experience in the job offered or in a Business Data Science-related occupation.
• Position requires 3 years of experience in the following:
  • Framing ambiguous business problems into tractable quantitative solutions
  • Querying databases and extracting large datasets using SQL
  • Statistical analysis using Python or R to build statistical and optimization models for solving business problems
  • Presenting technical analysis and statistical concepts to non-technical audiences and senior leadership
  • Creating data dashboards for exploratory data analysis