Machine Learning Analyst, Messages Spam and Abuse

2 weeks ago

Kirkland, Washington, United States Google Full time $147,000 - $216,000
Minimum qualifications:
  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
  • Experience in one or more of the following languages such as SQL, Python, Golang.
Preferred qualifications:
  • Master's degree in a quantitative discipline.
  • 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 5 years of experience working on signal development, data analysis, cyber security or anti-abuse.
  • Knowledge in Messaging, Rich Communication Services (RCS) or SIM, and Phone Number fraud and abuse.
  • Excellent communication skills, with the ability to effectively and concisely convey the results both verbally and in writing.
  • Excellent problem-solving, critical thinking, and attention to detail in an ever-changing environment.
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.

As a part of the Messaging Spam and Abuse team, you will work to prevent phishing, malware and scams across Google's messaging ecosystem. You will build detection and enforcement systems, partner with engineering teams to identify abuse risks, and develop scalable solutions to protect users. You will apply your machine learning and problem-solving skills in a global, cross-functional environment.

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.
The US base salary range for this full-time position is $147,000-$216,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities:
  • Develop understanding of abuse in messaging, current workflows and available intelligence.
  • Develop and deploy AI-powered solutions to identify malicious messages or improve operational efficiency, including feature generation and engineering, model development, model evaluation and deployment, maintenance, and adversarial robustness.
  • Drive outcomes for cross-functional projects by building stakeholder relationships and developing project roadmaps.
  • Establish and utilize key metrics to evaluate model changes, guide launches and inform decision-making.
  • Communicate effectively with technical and non-technical audiences at various levels, including producing write-ups, dashboards, and data visualizations to convey findings and recommendations to the team and cross-functional stakeholders.

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