Data Analyst, Risk

MINIMUM QUALIFICATIONS:

* Bachelor's degree or equivalent practical experience.
* 2 years of experience in data analysis, including identifying trends,
generating summary statistics, and drawing insights from quantitative and
qualitative data.
* 2 years of experience managing projects and defining project scope, goals,
and deliverables.

PREFERRED QUALIFICATIONS:

* Master's degree in a quantitative discipline.
* 2 years of experience or familiarity with one or more of the following
languages: SQL, R, Python, or C++.
* 2 years of experience or familiarity with machine learning systems.
* Excellent written and verbal communication skills.

ABOUT THE JOB:

Trust & Safety team members are tasked with identifying and taking on the
biggest problems that challenge the safety and integrity of our products. They
use technical know-how, excellent problem-solving skills, user insights, and
proactive communication to protect users and our partners from abuse across
Google products like Search, Maps, Gmail, and Google Ads. On this team, you're a
big-picture thinker and strategic team-player with a passion for doing what’s
right. You work globally and cross-functionally with Google engineers and
product managers to identify and fight abuse and fraud cases at Google speed -
with urgency. And you take pride in knowing that every day you are working hard
to promote trust in Google and ensuring the highest levels of user safety.

At Google we work hard to earn our users’ trust every day. Trust & Safety is
Google’s team of abuse fighting and user trust experts working daily to make the
internet a safer place. We partner with teams across Google to deliver bold
solutions in abuse areas such as malware, spam and account hijacking. A team of
Analysts, Policy Specialists, Engineers, and Program Managers, we work to reduce
risk and fight abuse across all of Google’s products, protecting our users,
advertisers, and publishers across the globe in over 40 languages.

RESPONSIBILITIES:

* Analyze and solve complex problems using data and statistical methods.
* Identify and prevent fraud and abuse.
* Improve tools and automated systems through data analysis, technical
expertise, and present to stakeholders.