Want to get hired at Google?

Business and Marketing Data Scientist

Google

New York, New York, United StatesOn Site

Original Job Summary

About the Job

Google's leadership team hand-picks thorny business challenges. As part of the BizOps team you will immerse yourself in data collection, draw actionable insights from analysis, and develop compelling recommendations for senior-level executives. You will support advertisers on YouTube by combining data, creativity, and advertising strategies to drive effective campaigns.

Responsibilities

  • Develop metrics to track and evaluate solution deployment.
  • Create dashboards and tools for process automation and reporting.
  • Curate and validate data to meet quality standards.
  • Collaborate with stakeholders to understand business goals and data context.

Minimum Qualifications

  • Master's degree in a quantitative discipline or equivalent practical experience.
  • 3 years experience with analytics, coding (Python, R, SQL), and statistical analysis.

Preferred Qualifications

  • 4 years experience in analytics with emphasis on advertising effectiveness, market research, or brand strategy.
  • Experience with machine learning models and AI techniques.
  • Ability to work with large datasets and manage multiple projects.
  • Proficiency in statistical analysis and data management.
  • Excellent project management skills.

Compensation

The US base salary range for this full-time role is $141,000-$202,000 plus bonus, equity, and benefits. Compensation details reflect base salary only. Additional details shared during the hiring process.

Key skills/competency

Business, Marketing, Data Science, Analytics, Advertising, AI, ML, SQL, Python, R

How to Get Hired at Google

🎯 Tips for Getting Hired

  • Research Google culture: Understand their mission and innovation via official channels.
  • Customize your resume: Highlight analytics, coding, and data science projects.
  • Demonstrate technical skills: Emphasize Python, R, SQL proficiency.
  • Prepare for interviews: Practice case studies and project management stories.

📝 Interview Preparation Advice

Technical Preparation

Review Python data science libraries.
Practice SQL and database querying.
Study statistical analysis methods.
Refresh machine learning model concepts.

Behavioral Questions

Describe a challenging project scenario.
Explain a time you collaborated across teams.
Discuss handling multiple projects simultaneously.
Share experience influencing senior decision-makers.