Job Description

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, including coding (e.g., Python, R, SQL), querying databases, or statistical analysis, or 3 years of work experience with a PhD.

Preferred qualifications:

• PhD in Statistics, Mathematics, Data Science, Economics, or a related quantitative field.
• 4 years of experience, including expertise with statistical data analysis on real-world data such as ML modeling, experimentation, sampling methods, and causal inference methods.
• Experience with machine learning on large datasets.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
  
• Ability to demonstrate leadership and self-direction, with a willingness to both teach others and learn new techniques.
• Ability to demonstrate skills in selecting the right methodology given a data analysis problem.

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.

Our team's mission is protecting billions of Android users from abusive applications, including malware, content abuse, impersonation, and behavioral abuse. Preventing abuse is fascinating technically and it features an adversarial scenario where we try to detect bad actors who try in turn to evade detection. Impact and success translates directly to keeping the Android ecosystem safe and protecting users from a variety of types of abuse.

As a Data Scientist, Research, you will have the unique opportunity to establish the foundation for data-driven decision-making and product innovation, directly impacting the safety of billions of Android users globally. Your day-to-day will involve data research, innovative solution development (such as improving and evaluating machine learning models), system optimization (e.g., enhancing enqueue and queue prioritization), review quality assessment (for both automated and human decisions), and a variety of other impactful projects requiring strong data and statistical acumen.

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

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