Business Data Scientist - Growth and Opportunity Marketing

• Provide support in media strategy, measurement and optimization that require expertise in advanced analytics work, with focus on marketing analytics methods and data science.
• Improve our understanding of our customers through user segmentation and cohort exploration. Identify and quantify our highest LTV customers, and help us acquire and retain them.
  
• Design and analyze controlled experiments or counterfactual causal inference studies to examine the incremental impact of our initiatives (marketing and GTM).
  
• Build the data infrastructure connecting complex first- and third-party user logs to enable your work. Build and improve the infrastructure that you will use for modeling and analysis.
  
• Work with a range of stakeholders ranging from technical to non-technical. Synthesize and communicate business insights from advanced techniques to executives in simple language.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 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.
  

Preferred qualifications:

• 4 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 managing end-to-end brand measurement frameworks, including brand lift studies, Marketing Mix Modeling (MMM), or Multi-Touch Attribution (MTA).
  
• Experience with LTV modeling and user segmentation analyses.
• Experience with querying and manipulating large data sets, particularly in SQL.
• Experience with root cause analysis to ensure that problems are solved at both tactical and strategic levels.

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.

As a Business Data Scientist supporting marketing projects such as the Google Career Certificate, you will lead projects, translate business problems into investigative solutions, and guide our team to implement data science solutions, improve data, and build models for business insights.

In this role, you will be directly responsible for providing data-driven decisions to empower our team’s business growth. You will perform advanced analytics on complex first and third-party data, stand up the data infrastructure enabling user insights mining, drive initiatives in experimentation and media measurement (MMMs, MMTs, etc.), and lead user segmentation modeling (through machine learning and other methods) in partnership with our Growth team.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 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.