Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.

Preferred qualifications:

• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

The team's mission is to protect Android users from abusive apps, including malware, content abuse, impersonation, and behavioral abuse. As a Data Scientist on the App Safety Engineering (ASE) team, you will work with multiple stakeholders across product, engineering, and operation teams to conduct data research, build methodologies into production systems to drive ASE key decisions and development. You will be responsible for translating data into recommendations and implementing process improvements.

Responsibilities

• Work with datasets, solve non-routine analysis problems, and apply quantitative methods as needed.
• Conduct analysis that includes data gathering and requirements specification, processing, analysis, model development and evaluation, as well as written and verbal delivery of results to business partners.
• Collaborate with stakeholders in Product Management, Engineering, and Operation teams to define questions, objectives, and metrics, identify and implement quantitative methods to answer those questions.
• Develop, own, and evolve methodologies and frameworks providing a source of truth measures for the organization.
• Establish comprehensive understanding of the production systems, advocate for changes where needed for product development, and build data driven solutions to faciliate the efficacy improvement.