Minimum qualifications:

• Bachelor's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, a related quantitative field, or equivalent practical experience.
• Experience conducting regression analysis.

Preferred qualifications:

• 2 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
  
• Experience conducting causal inference analysis.

About the job

Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems--from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can--changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.

Responsibilities

• Design and execute studies to measure the causal impact of our marketing on key brand metrics like awareness, favorability, and consideration, using techniques from survey analysis to quasi-experimental methods.
• Use ML to understand what features of viewer marketing campaigns drive the most impact on both brand perception and behavior especially among causal users of YT.
• Design experiments and measure the efficacy of cultural moments as a wrapper for other marketing initiatives at YouTube (e.g., shorts creator adoption, genai tools).
• Measure impact of paid and organic social marketing on driving viewer growth and understand opportunities for converting viewers to YouTube from competitors.
• Work with Product and Marketing to design experiments around insights.