Job Description

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 8 years of work experience using analytics to solve product or business problems, including coding (e.g., Python, R, SQL), querying databases, and statistical analysis, or 6 years of work experience with a PhD.
• 3 years of experience as a people manager within a technical leadership role.

Preferred qualifications:

• PhD in Statistics, Mathematics, Data Science, Economics, or a related quantitative field.
• Experience across a wide range of data science problems, including metrics design, experimentation, statistical or machine learning modeling, and non-routine data analysis.
• Experience articulating and translating business questions and using statistical techniques to arrive at answers using available data.
• Ability to provide detailed technical guidance to a team, enabling them to execute.
• Ability to select optimal statistical tools for data science problems.
• Commitment to continuous learning, respect for science, tolerance for ambiguity, and a deep interest in the practical application of science to business.

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.

With your leadership and professional expertise, you manage a team of analysts, plan project goals and lead the overall strategy for your group. You are a recognized authority in your functional area and develop, organize and launch projects that span engineering and analysis.

As a Data Scientist Manager, Research on the Android Ecosystem Trust team, you will lead the team dedicated to protecting billions of Android users from threats such as malware and impersonation in this new era of AI. You will drive technical strategy and foster innovation by identifying the most impactful data science problems worth answering, ensuring that research efforts are strategically aligned with defeating adversaries. By leading the development of advanced quantitative methods—including data mining, experimentation, and statistical and machine learning modeling—you will shape new data-driven products and solutions. You will act as a bridge between data and action, collaborating cross-functionally with Engineering and Product leaders to translate research findings into high-impact business decisions.

Android is Google’s mobile operating system powering more than 3 billion devices worldwide. Android is about bringing computing to everyone in the world. We believe computing is a super power for good, enabling access to information, economic opportunity, productivity, connectivity between friends and family and more. We think everyone in the world should have access to the best computing has to offer. We provide the platform for original equipment manufacturers (OEMs) and developers to build compelling computing devices (smartphones, tablets, TVs, wearables, etc) that run the best apps/services for everyone in the world.

Responsibilities

• Collaborate cross-functionally with leaders, engineers, and product managers to identify design opportunities and assess improvements for Android Ecosystem Trust systems and products.
• Define relevant Data Science questions about defeating adversaries. Lead the team to develop and implement quantitative methods (e.g., data analysis, experimentation, statistics, and machine learning modeling) to answer those questions and shape new data-driven products and solutions.
• Make business recommendations through data, with effective presentations of findings to multiple levels of stakeholders to drive business decisions and stakeholder thinking.
• Act as a thought partner to organizational leadership. Help leaders crystallize analysis insights and research findings into strategic decisions and communicate them to high-level audiences.
• Help the team realize their potential by setting clear expectations, openly evaluating performance, upholding accountability, and providing opportunities to stretch their skills.