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.
- Experience working in Machine Learning (e.g., tools, prepare training sets, train classifiers, etc.).
- Experience using Python libraries (e.g., Numpy, Scikit, and Pandas) for data exploration and visualization.
- Experience with statistical data analysis such as linear models, multivariate analysis, sampling methods.
- Ability to select statistical tools to solve data analysis problems.
- Excellent leadership skills, with the ability to both teach others and learn new techniques.
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About the job
The mission is to keep users on Google's platforms safe by creating data insights to fight fraud and abuse more efficiently and effectively. The team collaborates with operations, product, and engineering partners to deliver trusted experiences to users through excellence in data analytics and operational knowledge. The team also creates and aggregates intelligence signals, and then uses them to build solutions that improve the efficiency and effectiveness of Trust and Safety operations to fight fraud and abuse.
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 diverse 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
- Leverage domain knowledge to extract features from datasets for use in abuse prevention efforts.
- Conduct analysis that includes data gathering, exploratory data analysis, feature identification, selection, and validation.
- Build and prototype analysis pipelines to provide insights at scale, develop an understanding of Google data structures and metrics, advocate for changes where needed for both product development and improvement.
- Interact cross-functionally with a wide variety of people and teams across Engineering, Policy, Analytics, and cross-product abuse fighting teams, and work with engineers to identify opportunities and improvements for design.